International Journal of Image Processing and Pattern Recognition
International Journal of Image Processing and Pattern Recognition (IJIPPR) eISSN: 2456-6985 Editor-in-Chief Dr. R. Madana Mohana Professor, Department of Computer Science and Engineering, Bharat Institute of Engineering and Technology, Ibrahimpatnam, Hyderabad, Telangana, India Email: rmmnaidu@gmail.com Click here for complete Editorial Board Scientific Journal Impact Factor (SJIF): 5.992 International Journal of Image Processing and Pattern Recognition (IJIPPR) is a journal focused towards the rapid publication of fundamental research papers on all areas of image processing and pattern recognition. It's a biannual journal, started in 2015. Journal DOI No: 10.37628/IJIPPR Readership: Graduates, Postgraduates, Research Scholars, in Institutions, and IT Companies Indexing: The Journal is indexed in Google Scholar, Index Copernicus (ICV: 60.92) Focus and Scope Image analysis Image understanding Digital image processing Text compression Still image compression Video image compression Feature extraction Pattern recognition Multi-scale signal analysis Hidden markov models Anisotropic diffusion Partial differential equations Self-organizing maps Linear discriminate analysis Quadratic discriminate analysis Submission of Paper: All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance. Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration. Papers are accepted for editorial consideration through email info@journalspub.com or nikita@stmjournals.com Abbreviation: IJIPPR Frequency: Two issues per year Editorial Board Instructions to Authors
An In-depth Exploration: Unveiling the Computational Resources Utilized in Cancer Research
It is a major challenge leading to mass morbidity and mortality in cancer, despite the efforts and clinical trials in the past. Cancer is a very crucial field of research that directly concerns human life that requires early identification, diagnosis, prognosis, and therapeutics. The resources and web servers play very important roles in cancer genomics and medical image analysis. Computational intelligence (is a set of computational methodologies involving design, application, and development used to solve complex real-world problems which is the best alternative in cancer research. Computational intelligence has immense potential in the field of medicine from predictive modelling of cancers and medicines through computational approaches to image and microarray analysis. Hence, various computational resources such as tools, software, databases, packages, and web servers, accessible publicly have led to scientific advancement. that help in the effective utilization of information and knowledge. These resources not only help in studying diseases but also in the analysis and diagnosis and development of personalized medicine. This article covers important computational resources including tools, software, databases, packages, and web servers in cancer research with a major focus on their nature and functioning in oncology. It will enhance awareness and implementing appropriate resources in future cancer research, safer development of therapeutics.
Copyright (c) 2024 International Journal of Image Processing and Pattern Recognition
Vehicle Number Plate Detection and Recognition System in Nigeria Using Deep Learning
With over 200 million citizens, Nigeria is one of Africa’s largest and most populous nations. It is now essential to automate traffic management due to the rising number of vehicles in the nation. With the use of a deep learning technique known as the YOLO method, the study was able to construct an automatic vehicle license plate identification system that will recognize each of the vehicle license plates when many vehicles are present in a particular image or frame. In light of the foregoing, we employed fruitful research approach to accomplish the intended purpose of this study. According to the method, a digital camera is used to capture an input image of a system that contains automobiles. The required license plate letters are then moved on the Graphics User Interface, and the full process is then carried out and simulated in MATLAB. The license plate is localized when the vehicle has been identified. The most advanced YOLO (You Only Look Once) object detector was used for localization and detection. The Python programming language was employed to build the system, and XGBoost was used as the classifier to train and test the extracted features. A total of 110 vehicle photos were employed for license plate localization. To assess the proposed method’s performance against the current approach, two performance metrics, Peak Signal-to-Noise Ratio and Success Rate (%), were utilized based on the trial results. The accuracy of detecting vehicles overall was 97.5%, while the accuracy of locating license plates was 96.9%. The performance of the segmentation approach was examined using the recovered license plates from the localization step, and the segmentation accuracy was 95.1%. From the results, we were able to accurately extract each license plate’s characters from the single image with 96.5% success.
Copyright (c) 2023 International Journal of Image Processing and Pattern Recognition
Sound-based Bird Classification Using Perceptual Features and Machine Learning: A Gaussian Mixture Model
Classification of birds based on their sound patterns and identification of bird species found in the chirping sounds of birds were experimented with using the feature extraction method. This work is beneficial in ornithology to study birds and their behavior based on their sounds. The proposed methods can automatically classify birds by different sound processing. The audio file of a bird’s sound recording is first cut into smaller samples, and then the Fourier transform is utilized to analyze the frequencies in each instance. Features are extracted through the Perceptual Linear Prediction Cepstral Coefficients method, which can identify specific characteristics unique to each bird species. The clustering process is done using the Gaussian Mixture Model employed here to classify the birds into the respective classes; the last method is applying the testing procedure. It has been done to test our model’s accuracy in categorizing the various birds by their sounds. This work on the classification of birds using respective sounds has provided high accuracy for most birds, and the overall accuracy is 92%.
Fake News Detection Based on 3-HAN Architecture Using Deep Learning Techniques
Fake news spreading is a common scenario we see nowadays in this modern era of social media and smart phones. According to the New York Times, fake news is described as “a made-up story with the intention of misleading, frequently with financial gain as the motive.” The problem is complex, given its varied interpretations across the globe. Hence, an effective system is needed to detect whether the given news is fake or real, and while doing a literature survey, I came across various research works that have been done to prevent the spread of fake news. In this article, we propose an approach that leverages machine learning algorithms and natural language processing to identify false or misleading information in news articles. Machine learning provides an effective solution to our problem, and in general, all systems use machine learning techniques in some way or another. In this article, the method of attention is introduced in NLP, and a new approach is introduced combined with machine learning algorithms. The approach is using a 3-layered HAN architecture, which is based on the three particles of a news article, that is, words, sentences, and paragraphs. In this project, the efficiency of this architecture is shown in the currently used models.
Detection of Brain Tumor Using K-means Clustering Algorithm
A brain tumor is a dangerous medical illness that, if not treated promptly, can be fatal. Therefore, it becomes essential to find the tumor early on in order to organize therapy as soon as possible. A brain tumor is the result of the growth of abnormal cells in the brain. Brain tumors come in many different forms. The majority of brain tumors are malignant (cancerous); however, some are benign (noncancerous). Due to its non-invasive nature and superior ability to depict internal tumor information, MRI is preferred among all other imaging modalities. Moreover, MRI was utilized to develop clear images of the body’s components. By using the proposed methodology, we are able to detect brain tumor and their location. Three stages make up the proposed methodology—Image acquisition: A data set’s MRI picture is used. Pre-processing: The image is changed to grayscale at this stage, and then the median filter is used to get rid of the impulse noise present in the image. Post-processing: At this phase, the K means clustering technique is used to segment the image, and features from the image are extracted utilizing morphological processes. K means clustering, a method for unsupervised learning, divides the unlabeled data set into various clusters, where “k” is the number of clusters. With the help of the MATLAB program, a graphical user interface for brain tumor detection is created by us. These tools alter the grey level and include additional unique filters in an effort to enhance the MRI image’s quality.
Crop Disease Classification and Detection Using Deep Learning Approach
Early diagnosis of plant diseases is essential because they have an effect on the evolution of their unique species. For the identification and classification of plant diseases, a variety of Machine Learning (ML) models have been employed, but with the advent of Deep Learning (DL), a subset of ML, this area of research now seems to offer substantial potential for increased accuracy. The 4th most common staple food consumed worldwide is the potato, one of the extensively consumed staple foods. Additionally, partly because of the global pandemic coronavirus, there is a considerable rise in the demand for potatoes worldwide. However, the main factor causing the harvest's fall in both quality and quantity is potato illnesses.
Criminal Face Identification Using Deep Learning and Machine Learning
Criminal face identification is a crucial task for law enforcement agencies to prevent and solve crimes. The traditional methods for identifying criminals using human expertise and eyewitness testimonies have several limitations, such as subjectivity, inaccuracy, and unreliability. Machine learning and deep learning techniques have yielded promising gains in facial recognition and identification applications in recent years. In this study, we propose a criminal face identification system that combines machine learning and deep learning algorithms to accurately identify criminals from surveillance camera footage. In the proposed system, facial features are extracted using a convolutional neural network (CNN) and a Haar cascade is employed. The proposed system is evaluated on a publicly available dataset, and the results show that it outperforms the state-of- the-art methods in terms of accuracy and robustness.
Fuzzy and Intuitionistic Fuzzy Operators with Applications in Breast Tumor Classification
Breast Cancer accounting for 14% of all cancers in women is the most familiar cancer among women in India. According to GLOBOCON data 2018, 1,62,468 new cases and 87,090 demises were accounted for breast cancer in India. The incidence rate of breast cancer starts rising in the early 30s and reaches a peak at the age of 50–64 years. In general, 1 in 28 women will develop breast cancer at some point in their lives. For the early detection of breast cancer fine needle aspirate (FNA), technology and mammography are widely used. In this study, fuzzy and intuitionistic fuzzy aggregation operators are discussed along with their applications in breast cancer classification. Basically, aggregation operators use Laplace or normal distribution to collect vague information about breast tumors. Along with tumor label i.e., malignant, or benign, this aggregated information is utilized to train logistic regression, support vector machine (SVM) and nearest neighbor classifiers. These aggregation operators are efficient in improving classification accuracy and can be applied to all types of breast cancer images dataset for the early finding of breast cancer.
Using Image Feature Selection through Deep Salient Region Detection Using Canny Edge Detection Framework
Salient object identification has received a lot of attention recently in computer vision because it offers quick solutions to several challenging procedures. It first identifies the scene's most prominent and attention-grabbing object, after which it segments the object's entire extent. The perception and processing of visual input is linked to visual saliency. This proposed work focuses on several convectional saliency detection methods that are useful in a variety of applications such as image segmentation, image compression, object identification, image classification, and image retrieval. Here we presented a new Canny Edge Based Contrast Object Saliency detection algorithm, Automatic segmentation, and enhancement of a large number of important objects in the image without expensive training materials. The proposed strategy is calculated on several image dimensions at a level of conspicuity criteria. Our approach beats other recent schemes in terms of precision and recall, Fmeasure, and accuracy, while staying simple, fast, and smart, according to experimental results.
NLP in Sign Language Recognition Boon for Dumb People
In order to help and provide facility to the persons who have vocal and hearing problems to communicate, this study provides a design innovative system. It discusses an improving technique for speech-to-sign translation and sign language recognition. The created method uses skin colour segmentation to extract indications from video sequences with a dynamic and minimally crowded background. It separates out the relevant feature vectors from dynamic movements and gives room in static motions. Support vector machines are used for this classification. Voice recognition is based on the pyramid standard module. According to experimental findings, signs may be satisfactorily segmented against a variety of backdrops, and gestures and speech recognition are comparatively accurate.
Foreign Material Detection for Scallion Processing Industry Using HSV Color Space
In this paper, an optical foreign substance detection system is designed to address the problem of foreign substances mixed in food. The five color spaces of RGB, HSV, YCrCb, CIELab, and CIELuv are compared by using scallion images and non-scallion images to seek the color space that is the most suitable for the scallion processing. The designed image algorithms are given to filter out food raw materials and background images. The images on the conveyor belt are captured by stacking backlight sensors. The technology of image filtering is based on HSV color space to determine the production line and whether there is a foreign substance or not. The purpose of this study is to help traditional human detection, reduce the omissions in inspections caused by fatigue, laziness, and other problems, and at the same time save labor costs, and increase production speed, which can help the food production lines reduce the problem of mixed foreign objects in food raw materials.
Image Processing Invisibility Cloak: A Review
We have seen invisible people in stories, but in the world, it is still not proven that invisibility exists. This review explains the invisibility of anything with the help of various technologies. On many commonplace commercial objects, you can find difficult patterns and backdrops. Such textual data may be presented in a variety of scales, fonts, colours, and orientations. The very basic operations are performed to process the frames, which include obtaining a live video feed, loading the frames one at a time, identifying different red colour shapes inside the live feed frames, and segmenting them using previously saved images. In this, anything which is required to be made invisible is covered or highlighted with material. With the help of projector, the background image on the object is used to make it transparent.
Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods
The presented work uses Deep learning methods to detect diseases in cabbage leaves. To avoid a reduction in agricultural product yield, disease diagnosis in plants is crucial. Manual plant disease monitoring is challenging and time-consuming at every stage. Five major types of diseases are considered. Initially, the input images are classified as healthy and diseased. Further, the diseased images are classified into five different varieties. Early and precise biotic stress detection is necessary for effective crop protection. These accomplishments pertain to the creation of non-intrusive, highresolution optical sensors as well as the development of data analysis techniques that can handle the resolution, size, and complexity of the signals from these sensors. The accuracies of 93.5 and 90.5% are achieved for healthy and diseased leaf images. The overall classification accuracy of 92% is attained. The developed methodology is found to provide good classification accuracy.
Facial Expression Recognition by Using Multifeatured Fusion And Deep Learning Techniques
In our daily lives, sentiment analysis is a vital part of communication. A vital component is information regarding how the user feels when someone communicates. You will need a solution that can handle everything from recognizing a user's emotional state to personalizing the user experience. This study's objective is to look into emotions. Today, deep learning techniques are quickly advancing in a variety of fields, including computer vision. Without a doubt, a convolutional neural network (CNN) model can analyse a photo and recognize facial emotions. Create a method that takes into account Understudy's external emotions. The FER2013 database contains seven behaviours in three phases: Hear Falls face recognition, standardization, and CNN emotion recognition. Teachers may customize their greetings to their students' moods, according to the research, and face expression detection can be taught.
Deep Learning in Medical Image Classification and Object Detection: A Survey
Deep learning methods have demonstrated superior performance in the area of computer vision, speech recognition, natural language processing, healthcare, and many more. Convolutional neural networks (CNNs) are a class of deep learning methods that have the ability to learn from raw data available as images, audio, or text. CNNs have become a powerful tool for variety of pattern recognition tasks due to the availability of abundant data and GPU-based training. Usually, a CNN is designed with the following: (1) Convolution layers, (2) Pooling layers, and (3) Fully connected layers. Convolutional layers use convolution filters to extract the low-level features (like edges, circles) and high-level features (like objects, texture) from the input. Pooling layers are interleaved in between the convolution layers to reduce the input dimension for the subsequent layers. A fully connected layer makes use of extracted features from the pooling or convolutional layer and maps them to the final output, such as in the case of classification. In the domain of medical imaging analysis, deep learning methods are rapidly becoming state-of-the-art, achieving magnificent performances in many medical applications amid the challenges of unavailability of large amounts of medical data, and lack of annotated data. In the present work, we seek to review the application of deep learning approaches in the domain of medical imaging. We highlight the impact of deep learning methods with respect to two key areas: image classification and object detection, and give comprehensive summaries of findings in these areas. Future research directions and solutions are also explored.
Copyright (c) 2022 International Journal of Image Processing and Pattern Recognition
Human Mood Identification (Data Recording System)
Researchers across a variety of fields are becoming more interested in a human-computer interface system for automatic facial emotion identification. In our project, an Automatic Facial Expression Recognition System (AFERS) which records emotion for physiological analysis has been proposed. The suggested process consists of four steps: Identifying every face in the frame is part of the first stage. We focus on each face and adjust it in such a way that even if the face is turned in a different direction it is still able to identify the person. In the second stage, we will make use of a deep face algorithm to detect the emotion of the person. Unique features of the faces are selected that can be used to differentiate people and compare them with the faces of people stored in our database to recognize a person. The recognition rate for this method is around 90–95%. In the third stage, we have our Attendance System which will record the emotion of the person after every 1 sec which can be used to understand the person psychologically. At our final stage, we have developed GUI using Tkinter for user interaction.
Future of Augmented and Virtual Reality in Our Social and Work Life
The future technology is expected to change not only industrial services, but also our social and work life. At the moment, virtual reality and augmented reality are the two biggest technological trends. In the past few years, there is a growing number of low cost augmented and virtual reality devices being available to the general public. With this, there is a huge potential of such devices being an important part of our lives. This research work contributes to explore how augmented reality and virtual reality will have an effect on our social and work life. The most common way to experience VR has been through smartphones and headsets since a few years ago. This has been the simplest option for people to begin utilizing VR. Compared to the market for augmented reality today, virtual reality is far more developed. To create an immersive VR experience, the necessary hardware platforms and software tools are now available. Increasingly sophisticated technology, like the Oculus Rift and 360-degree cameras, are making virtual reality experiences more widely applicable in our daily lives.
Deep Learning to Detect Pneumonia from Chest X-Ray Image
Pneumonia is an acute pulmonary infection that is caused by viruses or by bacteria and it causes inflammation in the lungs. In 2019, a total of 2.5 million people died because of pneumonia, out of which, one third were younger than the age of 5. This infection was diagnosed by taking a chest Xray. The prediction of diseases is mainly based on human knowledge. In this, the probability of human error is quite high. Also, in remote areas, expert doctors are not always available. Our project is about creating a deep learning algorithm-based learning model which can detect a patient with pneumonia by analyzing its X-ray image. We have created CNN and VGG16 based models to predict if the patient is suffering from pneumonia or not. Our proposed system will provide accurate and rapid diagnosis, ensuring timely access to treatment and potentially saving lives.
Review on Sign Language Detection Using Action Detection
The development of sign language as a means of communication for those with hearing loss has been a significant step. Sign language is always needed for communication as not everyone understands how to translate signs. Without an interpreter, it is challenging to communicate. The hand shape, motion profile, and positioning of the hand, face, and other body components vary amongst signs in a particular sign language. Therefore, visual sign language detection is a difficult area of computer vision research. To overcome this, we need a flexible and long-lasting solution. For barrier-free communication, sign language must be translated so that it may be utilised by the broader population. The two primary methods for detecting sign language are image-based and sensor-based strategies. An image-based solution employs one or more cameras to record a series of images of the signer making the sign, which are then processed by image recognition software to identify the sign. The sensor-based approach tracks the hand articulations using instrumental gloves that have sensors built in. This project's primary goal is to break down barriers that separate the deaf and dumb from the rest of society
Skin Disease Detection Using Image Processing
Skin issues incorporate a wide extent of indications and seriousness. They can be pleasant or unsavoury, and they can be momentary or constant. The skin is the human body's largest organ. Between 30 and 70% of people are influenced by the skin related issues. Skin-related health issues affect people all over the world, necessitating quick and effective resolution. As of late, computeraided demonstrative (CAD) innovations have been used to effectively recognize skin cancers in dermatoscopic pictures. Be that as it may, there has been very little inquire about on the foremost common skin infections in clinical pictures taken with cheap cameras or cell phones. To ensure that a CAD system's estimates are reliable, dermatologists must be able to obtain accurate representations of skin damage. We propose viable representations based on dermatological demonstrative criteria to address this issue.
Recognition and Detection of Content in Video Using OpenCV
The emergence and continued reliance on the Internet and related technologies has resulted in massive amounts of data that can be analysed. Humans, on the other hand, do not have the cognitive abilities to comprehend such vast amounts of data. Machine learning (ML) is a mechanism that enables humans to process large amounts of data, gain insights into the data's behaviour, and make more informed decisions based on the analysis's results. ML has a wide range of applications, such as efficient and accurate object detection, and has been a hot topic in the advancement of computer vision systems. Since the introduction of deep learning techniques, the accuracy of object detection has increased dramatically. The project intends to incorporate cutting-edge object detection techniques with the goal of achieving high accuracy with real-time performance. The reliance on other computer vision techniques to assist the deep learning-based approach is a major challenge in many object detection systems, resulting in slow and suboptimal performance. The resulting system is fast and accurate, making it useful for applications that require object detection.
Investigation and Classification of Cardiac MRI Images using Texture Analysis
In order to quantify the myocardial viability after infarction, recent researches have developed new cardiac MRI protocol using particle contrast agents which highlight the different areas after infarction: i.e. the healthy area, the necrosed area and the two ischemic areas. Actually the researches are carried out on rodents micro cardiac MRI for which the obtained images are very noisy and present a very low contrast. In this paper, we analyse the potential of texture analysis to classify each pixel of the myocardium, on rodents micro cardiac MRI, in one of the three areas. In a single image, the region of interest is selected, i.e. ROI and find linear separatblity coefficient and apply Linear Discriminant Analysis on selected region of interest which gives exact classified area in the image. Then we performed Texture analysis is used to separate the unwanted region from the desired region, allowing the desired region to be found. Texture refers to the representation of an image or the concepts that surround it. Texture analysis is used extensively in object recognition, surface defect detection, pattern recognition, medical image analysis, and other computer vision applications. Three types of ROI in a single image with different pixels are considered for Texture analysis. An image is divided in to two distinct areas each has different texture. Now texture image is segmented through which the image boundaries are defined and areas are compared. If the boundaries and other characteristics are different the range of the boundaries can be defined. The technique of "Texture Segmentation" divides an image into distinct areas, each with a different texture. Texture segmentation is the process of defining the boundaries of various textures. In other words, texture segmentation compares the features of the boundaries and areas and determines the boundary range if their texture characteristics are sufficiently different. The texture analysis was then applied to a very small size images does not seem to be a way for further searches. But we can expect better results on human cardiac MRI for which resolution and contrast would be much better.
A Report on Object Detection and Caption Generator
Image Caption Generator is a application that generates captions for images. The image's semantic information is recorded and transformed into plain language. The capture mechanism is a timeconsuming effort that combines picture processing and computer vision. The mechanism must be capable of detecting and establishing links between items, humans, and animals. The goal of this article is to use deep learning to detect, recognize, and generate meaningful captions for a given image. The Regional Object Detector (RODe) is used to detect objects, recognize them, and provide captions. Deep learning is used in the suggested method to improve on the existing image caption generating system. Experiments are carried out on the Flickr 8k dataset using the Python programming language to demonstrate the proposed strategy.
Biometric Recognition with Iris Capturing Techniques
The primary Iris acknowledgment appeared in 1995, the iris. As it utilizes human organ, Iris acknowledgment is viewed as a type of biometric confirmation. The Indian government likewise utilizes biometric frameworks for the recognizable proof of residents in various application territories like as rashan shop, Aadhar venture, in various government test structures and enlistment departments and so on. The inspiration driving the article is to give an audit of the most precise and make sure about methods for biometric distinguishing proof which is none other than the Iris acknowledgment framework. Sharp gathering contraptions have gotten extending excitement for improving the security framework for a more astute city. Right now age everything required must be adequately splendid.
Smart Video Surveillance System in Banks
This review paper represents an aristocratic approach for acute video surveillance system and human activities automatic recognition from video sequences and has an embellish presence in every sphere of life. This paper is reviewed about video monitoring systems with its uses and kinds of video superintendence techniques. But we have mainly concentrated on acute video surveillance technique based on summarization because it provides behavioral analysis capabilities based on any videos. Also, it reduces the search time of conversion of content based video improvement problem to content based image improvement problem. We have also made acquainted with a chronicle study of acute video surveillance systems in banks, which helps find fraudsters and the pedestrian recognition system. Video analytics technology (IVS) is used in a surveillance system to analyze a video frame or a group of video frames to detect, classify, recognize or identify a predetermined event that has been programmed through the software. It uses mathematical algorithms to detect moving objects in an image and filter non-relevant movements. It then creates a database that records the attributes of all the objects detected and their movement. This infrastructure helps to reduce wiring costs and the bandwidth required in the video surveillance network. In short, video analytics technology will enable users to manage a vast mass of undifferentiated data that they may receive from different cameras and turn it into useful information. This will turn their cameras into predictive tools that will allow them to spot problems brewing and prevent incidents providently, rather than just filming the events for later investigative use.
Facial Image Analysis for Identification of Gender: A Machine Learning based Approach
Facial biometrics for gender classification is known to significantly improve the efficiency of person identification in biometric access control models. It not only increases the speed but also improves the accuracy. It reduces the effort of finding a match for a face in the databases to almost a half. The process like any pattern recognition framework needs to extract the useful features. A novel gender identification algorithm is proposed which is based on the facial features of a person. Viola Jones object detection technique is used for face detection from an image, and the relevant facial features are extracted using Topographic Independent Component Analysis. An SVM based classifier is trained using the calculated feature vector to determine whether the person in the image is a male or a female.
Copyright (c) 2021 International Journal of Image Processing and Pattern Recognition
Research Paper on Handwritten Character Recognition
Handwriting recognition is the capacity of a machine to get and decipher penmanship contribution from numerous sources, for example, paper records, photos, touchscreen gadgets, and so on. Handwriting and automatic character recognition are an emerging area of research and find many applications in banks, offices and industries. The main objective of this project is to design an expert system capable of efficiently recognizing a format character of a particular type using the artificial neural network approach. Use neural signs in the field of literature. Reduced manpower to manually convert ancient literature to digital form. The proposed system served as a guide and work in the character recognition areas. Enrich the digital library with the English language. Neural computing is a relatively new field and therefore design components are less determined than those of different structures. Neural PCs carry out information parallelism. Neural PCs work in a totally extraordinary manner from ordinary PCs. Neural PCs are prepared (not modified) so that, given a specific beginning state (information passage); classify the input data into one of the classes or evolve the original data so as to optimize some desirable property.
Need of Face-Mask Detector Model to Tackle COVID-19 Situation in India
The COVID - 19 epidemic is affecting everyone whether rich or poor, strong or weak. The use of a mask can surely aid in the control of the spread of the virus. This becomes even more important for a country like India where the total population is above 135+ crores and due to lockdowns and various other reasons, a huge percentage of the population has dipped below the poverty line and health standards of the poor are declining day by day and all these factors are contributing to an uncontrolled outbreak of coronavirus in India. As the government has already mandated the use of masks as a stop-gap measure to control the pandemic but due to the carelessness of the people they can be categorized into 3 groups based on the use of a mask. Specifically, a person who is properly wearing a mask, or is wearing a mask but improperly, or is not wearing a mask at all. By deep learning, we can make a model which detects masks accurately without human-to-human interactions and can help in controlling this corona pandemic. The Face mask detection system algorithm can be used to detect faces in still images as well as live video streaming and can tell whether the mask is worn properly or not. The mask detector model used should be as flexible as possible and on the same hand simple in structure, so that it can be used by a camera monitoring 1 person at a time or by a public CCTV monitoring hundreds of people at a time. Considering mass screening is possible, it may be utilized in congested areas such as transport hubs, bus stops, supermarkets, sidewalks, mall entrances, schools, and campuses. We can ensure that a person wears the face mask correctly and in a proper way and contribute to minimizing the spread of this destructive virus.
Blind Assistance System using Tensorflow
Because of item recognition's cozy relationship with video investigation and picture understanding, it has drawn in much exploration consideration lately. Customary item discovery techniques are based on carefully assembled highlights and shallow teachable designs. Their exhibition effectively deteriorates by building complex outfits which join numerous low-level picture highlights with undeniable level setting from object finders and scene classifiers. With the fast improvement in profound learning, all the more amazing assets, which can learn semantic, significant level, further highlights, are acquainted with address the issues existing in conventional models. These models act contrastingly in network engineering, preparing procedure and improvement work, and so forth. In this paper, we provide a review on deep learning based object detection frameworks and how it can be used to help the visually impaired. Our audit starts with a short presentation on the historical backdrop of profound learning and its delegate apparatus, to be specific Convolutional Neural Network (CNN). We have used Tensorflow made by google which makes this task even faster and easier to implement. We have chose to dedicate this for the blind because we think they are the most neglected when it comes to project helping people with some form of disability and this group of people are at more risk of doing everyday tasks as they don’t know what is in front of them and also they can be fooled easily for example someone can give them wrong amount of currency and many more things so we have decided to help them and make them more independent.
A Multi-Terrain Fractal-Based Digital Camouflage Pattern
A recent need for modern infantry operations would be the requirement of an effective camouflage pattern for usage in multiple terrains. This is a result of the modern evolution of military combat as its common for military units to be rotated from one location to another (usually with drastically different ecosystems). A good camouflage pattern that has tried to address this issue would be Crye Precision’s MultiCam, however it still has its limitations. This paper addresses this issue and develops a digital fractal-based camouflage pattern with multiple carefully designed layers and inspired by the abovementioned MultiCam. This proposed camouflage pattern shows considerable efficiency (theoretically) in operations for terrains like urban and semi-arid locations when compared to other commonly used designs.
Identifying the Age Using Face Image Using DWT, LBP, SVM and k-NN
ABSTRACT The research related to identifying the age using face images has become progressively more important, due to the fact it has a multiplicity of potentially helpful applications. In this paper we survey the complete age synthesis or age estimation from face images techniques. Automatic facial age identifying is one of the main issues in pattern recognition which shows the age of human according to her/his facial expressions. Basically, age detection/estimation is a process of identifying the actual (or approximate) age of human. An identifying the age system is usually composed of aging feature extraction and feature classification; both of which are important in order to get better the performance. Facial features are measured one of the important personal behavior. This can be used in numerous applications, like face recognition and age estimation. The importance of these applications lies in numerous areas, like defense applications, law enforcement applications, and attendance systems. Identifying the age is classification of a face image with exact actual age or age group. In this paper, we are going to plan a system to identify the age using face images which is based on Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) for feature extraction and Support Vector Machine (SVM) and k-NN (k-nearest neighbor) for classifier. Our planned approach has been developed, tested and trained using the database FG-NET. Keywords: DWT, LBP, SVM, k-NN, Feature extraction, Classification
Land use changes in the suburbs of Urmia in northwestern Iran using Landsat image processing
ABSTRACT Investigating land use/land cover changes in the suburbs is one of the interesting topics Attention is especially for city managers. Satellite image processing it is the most important source of information in this regard. In this study, Landsat 7 and 8 satellite images were used. Initially, preliminary projections including geometric and radiometric corrections were performed. After selecting the ground control points then using the max method Similar image classification was performed for 2000 and 2020 and 4 vegetation classes, landscaped, Rocky and barren lands were identified. Comparing the two land use maps, the changes show that the city during this period it has expanded a lot, so that user conversion bare soil and vegetation to the constructed lands were 1642 and 1603 hectares, respectively. The results show that if the changes continue in the same way the study area will be further transformed in the future. Keywords: Remote sensing, land use, change recognition, Urmia, Landsat
Integrated PM2.5 Estimation Using Satellite Images, Pollution Station Data, Meteorological Data and Artificial Neural Network (Case Study: Tehran City)
ABSTRACT Nowadays, the use of remote sensing to monitor airborne particulates by spatial continuity is made possible by the MODIS Sensor Aerosol Optical Depth (AOD) products. In this study, a simplified aerosol estimation model (SARA) was used to estimate the aerosol optical depth. For this purpose, the MODIS sensor data as inputs to predict PM2.5 near the surface, aerosol optical depth data and meteorological data (wind speed and direction, air temperature, planetary boundary height and relative humidity) and artificial neural network are used. The results of comparing the aerosol optical depth obtained with real PM2.5 data obtained from the contamination stations showed that the highest correlation was related to summer with Pearson coefficient (0.67) and the least Pearson correlation coefficient (0.55) to spring. The results also showed that the use of meteorological data and artificial neural network for predicting land surface PM2.5 was successful. Comparison of PM2.5 output of artificial neural network model with observed PM2.5 values showed seasonal variation with R2 coefficient of 0.51, 0.74, 0.61, 0.62 and RMSE value of 15.2, 7.5, 12.1, 6.59 shows the spring, summer, autumn, and winter seasons, respectively. Keywords: PM2.5, Artificial Neural Network, MODIS, Tehran, SARA Algorithm
Convolutional Neural Network Algorithm for Stress Field Approximation for a Uniaxially Loaded Plate with Holes
ABSTRACT Knowing the high stress concentration and its location around a hole is of practical importance in designing engineering structures, effective parameters can be selected in order to achieve minimum stress concentration around the hole(s). The visualisation of the stress distribution is done experimentally or numerically using finite element analysis (FEA). This study involved the approximation of the von Mises stress field distribution around regular holes in finite metallic plates, based on deep learning 2D encoder-decoder convolutional neural network (CNN). The CNN based stress field prediction is achieved by nonlinear 2D image training data obtained from ABAQUS, the geometric parameter of flat plate and the hole(s) are the input to CNN and the von Mises stress filed distribution is the output, assuming the plate considered is finite, isotropic, plane stress state, linearly elastic and uniaxial loading condition with total number of trainable parameters for the neural network is 3,437,143. The prediction accuracy achieved is 94.18% with a loss of 0.03% between the ground truth and the predicted images Considerable care was taken to minimize the complexity of the CNN architecture and to make it interpretable and to achieve high accuracy with available low data. The proposed method can predict acceptable accurate solution to a problem with the geometries not included in the training dataset. This approach can be directly applied as it provides immediate feedback for real-time design iterations at the early stage of design. Keywords: Convolutional neural network, finite element analysis, isotropic plate, machine learning, regular hole, stress concentration, uniaxial loading
Local Binary Patterns and Its Extended Variants
ABSTRACT This paper focuses on the Local Binary Patterns and its various important variants. LBP is a non-parametric descriptor and used to extract, analyze, recognize and classify the different modality images. It summarizes the local patterns of image characteristics efficiently. LBP and its many extended versions have been extensively used in numerous applications of computer vision, image processing, pattern recognition and biomedical field in recent years. Very discriminative and computationally efficient local texture descriptors based on local binary patterns (LBPs) is studied, which led to significant progress in applying texture methods to different problems and applications. The efficiency and usability of the LBP operator and its success in various real world applications has inspired the development of much new powerful LBP variants. In this paper, the important extensions of LBP using local structure of the image are extensively reviewed. Keywords: Local binary pattern, texture, LBP, LTP
Facial Expression Classification using Multi-Scale Histogram of Oriented Gradients
An automatic facial expression classification method, based on multiscale Histogram of Oriented Gradients (HOG) features extracted from sub-facial image patches, is proposed in this paper. A number of multiclass Support Vector Machines (SVM) are designed using the multiscale HOG features for classifying six basic facial expressions for frontal face images. The proposed method, with a simple design and few training data, achieves descent classification accuracy at a very low computational complexity. Keywords: multi-scale HOG, Support Vector Machine, Sub-facial patch, histogram. Cite this Article: Sagar Deep Deb, Manish Sharma, Chandrajit Choudhury, Fazal Ahmed Talukdar, Rabul Hussain Laskar. Facial Expression Classification using Multi-Scale Histogram of Oriented Gradients. International Journal of Image Processing and Pattern Recognition: 2020; 6(1): 5–13p.
Copyright (c) 2020 International Journal of Image Processing and Pattern Recognition
A Simple and Fast Skull Stripping Technique for Brain MR Images
Skull stripping is an important aspect of study in the field of Medical image processing. It eliminates the present non-cerebral tissues from the MR image to improve the speed and accuracy of the algorithm. An unsupervised double segmentation based method has been proposed for faster processing. Threshold values required for double segmentation are obtained using the Otsu algorithm. This method avoids the conventional Morphological Opening and Morphological Closing operations to enhance the speed of the algorithm. Experimental result shows that the proposed system can strip the skull portion from a brain MR image at 0.155 seconds. Keywords: Skull, unsupervised, segmentation, otsu, opening, closing. Cite this Article: Sushanta Debnath, Fazal A. Talukdar. A Simple and Fast Skull Stripping Technique for Brain MR Images. International Journal of Image Processing and Pattern Recognition. 2020; 6(1): 1–4p.
Review Paper on Image Enhancement Using Various Methods
Image enhancement methods were used to modify the images that were accepted as visual perception. Image enhancement methods mainly used for image contrast. Histogram compensation, used in the form of the cumulative distribution function (CDF), is one of the challenging factors in image processing. The goal of the enhancement is to improve the structural look of an image without impairing the input image. The enhancement techniques make it easier to identify key features by removing noise and other artifacts from an image. This article discusses studies to implement image enhancement in specific areas such as satellite imagery, infrared imagery, medical imagery and digital entertainment imagery, as well as information from the image enhancement method used. Keywords: Image enhancement, AHE, Histogram Equalization, CLAHE, spatial domain image, frequency domain method, etc. Cite this Article: Manish Sharma, Ashish Dubey, Prashant Badal. Review Paper on Image Enhancement using Various Methods. International Journal of Image Processing and Pattern Recognition. 2020; 6(1): 27–35p.
Investigations of Diabetic Retinopathy Algorithms in Retinal Fundus Images
Diabetic Retinopathy (DR) location is a point of high enthusiasm for therapeutic picture investigation since the examination of DR is essential for finding, treatment arranging and execution, and assessment of clinical results in ophthalmology. Programmed or self-loader DR location can bolster clinicians in playing out these undertakings. Diverse restorative imaging methods are at present utilized in clinical practice and a suitable decision of the identification calculation is required to manage the embraced imaging procedure attributes. This paper goes for auditing the most recent (last three years) and inventive DR location calculations. Among the calculations and methodologies considered, this paper profoundly examined the most novel DR identification including feature extraction, filtering, machine learning, pattern coordinating, wavelet, statistical estimation, clinical examination and miscellaneous techniques. This paper examines in excess of 50 ongoing articles concentrated on DR identification techniques. For each examined methodology, synopsis tables are exhibited revealing imaging strategy utilized, anatomical locale and execution estimates utilized. No single identification approach is reasonable for all the distinctive anatomical area or imaging modalities, subsequently the essential objective of this survey is going to give an up and coming wellspring of data about the cutting edge of the DR discovery calculations with the goal that the most appropriate strategies can be picked by the explicit errand. Keywords: Diabetic retinopathy, feature extraction, filtering, machine learning, pattern matching, wavelet, statistical estimation. Cite this Article: N. Jagan Mohan, R. Murugan, Tripti Goel. Investigations of Diabetic Retinopathy Algorithms in Retinal Fundus Images. International Journal of Image Processing and Pattern Recognition. 2020; 6(1): 14–26p.
Video Coding with Packed Stream in Image Processing and Applications
Scalable video coding is a technique which allows a compressed video stream to be decoded in several different ways. This ability allows a user to adaptively recover a specific version of a video depending on its own requirements. Video sequences have temporal, spatial and quality scalabilities. In this work we introduce a novel fully scalable video codec. It is based on a motion-compensated temporal filtering (MCTF) of the video sequences and it uses some of the basic elements of JPEG 2000. This paper describes several specific proposals for video on demand and video-conferencing applications over non-reliable packet-switching data networks. Keywords: Video, compression, scalability, JPEG 2000, video on demand Cite this Article: T. Velumani. Video Coding with Packed Stream in Image Processing and Applications. International Journal of Image Processing and Pattern Recognition. 2020; 6(1): 36–44p.
In today’s world, there is a growing need of security and protection for people. Even though the technologies are most advanced in current era, the much effected problem faced by every people in a country like India is, their own protection. The criminal activities on humans and animals are increased day-by-day. Also, disasters like natural and man-made causes lot of problems. The fate of these acts is uncontrollable. So, we introduce latest drone technology for this purpose. The UAV used will be a composition of different technologies that works at same rhythm to get much effective performance. This semi-automated drone will be able to control by the officials in emergency situation, for taking immediate actions and also arming the drone. The simple weapon on the drone can only be controlled by the officials. There are many features that can only be controlled and used instantly by the officials in the control room. That is why it is called a semi-autonomous drone. It provides a real-time monitoring system with minimum latency. The different versions of this drone make the drone to be used in different situations and sectors. Keywords: quadcopter, UAV, waypoints, surveillance, drone, live streaming Cite this Article: Midlaj Nazar, Fadil M.N., Jithin T.V., Jashid M., Athira B. Kaimal M. Surveillance System Using UAV. International Journal of Image Processing and Pattern Recognition. 2019; 5(2): 27–33p.
Identifying Number Plates using Feature Extraction
A number plate can be recognized completely only when the number plate on the vehicle is neither broken nor deliberately mutilated. This pose a hinderance in recognizing these types of plates. In order to overcome this problem, one has to either compare the characters recognized from the broken plate or directly compare the features of the given plate to the database. This helps in reducing time and space complexity. Various feature extraction algorithms are available that are FAST, EIGEN, SURF, HARRIS, BRISK, MSER. Out of which SURF (speeded up robust features) is preferred. In our proposed approach, we compared features of given plate with the saved plates features. Based on the matching accuracy best matched plate is selected as output. Keywords: image processing, number plate extraction, feature extraction, SURF, fast, eigen, Harris, brisk, MSER Cite this Article: Saurabh Allawadhi. Identifying Number Plates Using Feature Extraction. International Journal of Image Processing and Pattern Recognition. 2019; 5(2): 20–26p.
Detection of Leaf Diseases Using Image Processing Techniques
Agricultural field plays a vital role in the earning to feed ever growing population. The productivity of the crop depends on pest attack, disease, weather, etc. The disease in plants will affect the production of crops. Detecting diseases in plants through visualization is a traditional method which is not relevant. We are focusing on reliable, fast and accurate detection method that can be done using image processing techniques to monitor large area of agricultural land. Methods that explore visible symptoms in leaves are considered in this paper even though it can manifest in any part of the plant. Disease detection involves the steps like image acquisition, pre-processing, segmentation, feature extraction and classification. The paper contains a survey on different disease classification techniques like SVM and neural network that will be useful for plant leaf disease detection. Keywords: SVM (support vector machine), neural network, RGB (red, green, blue), BPNN (back-propagation neural networks), image processing, SGDM (spatial gray-level dependence matrices) Cite this Article: Lamiya J.A., Pooja R. Nair, Suraj K. Yadav, Usama Yoosuf, Sethulekshmi R., Rafeeq Ahmed K., Riyaz A. Rahiman, Vishak K.H. Detection of Leaf Diseases using Image Processing Techniques. International Journal of Image Processing and Pattern Recognition. 2019; 5(2): 14–19p.
Arduino Based Smart Automatic Ration Distribution System Using Fingerprint Sensor
Today’s ration distribution system faces several challenges and plenty of problems like non legal importing and corruption of products happens in the ration distribution centers in India. These controversies embrace irregular activity of the products, wrong entries within the manual stock register. Alternative times the particular product provided by the government for the distribution does not reach the common people. In our project, we have got replaced the manual work wiped out the distribution centers by good measurement machine-driven electronic device and secured device by the Aadhaar card that is provided by the government. With the assistance of Arduino microcontroller, the machine measures the commodities accurately and updates it in data base sporadically regarding the provision of products and knowledge regarding the transactions wiped out a digitalized manner. Here, to possess access to the information and data relating to the stock, a main data base is formed which may be access by each common customers of that exact section and by the government mainstream invigilators for distribution centers from their head workplace. Therefore, this project ensures corruption free ration centers operating system which can conjointly enhance the direct communication of the customers with the government and can contumaciously give transparency. Keywords: automatic rationing, fingerprint detection, digitalization, accurate-solid and liquid measuring module, online verification method Cite this Article: Athira Aravind, Abhirami T. Jinendran, Ashwini, Vivek Tom, Reneesh C. Zacharia. Arduino-based Smart Automatic Ration Distribution System using Fingerprint Sensor. International Journal of Image Processing and Pattern Recognition. 2019; 5(2): 7–13p.
An Assistant Reading System for Visually Impaired People
In the real world, books and documents are the sources of knowledge. This knowledge is only bounded to people with clear vision but not for visually impaired people in our society. As a God’s gift we gain many advantages with our vision. Our society includes a group of people who does not have a clear vision or people who are visually impaired. For this group, world is like a black world. They have no idea about anything or shape or any language. The only way to get information for them is Braille. As a common courtesy, Braille is one of the much expensive way for people. The solution is rather simple, introduce a smart device with a multimodal system that can convert Malayalam text document into audio. A blind can read document only by capturing words which is then audibly presented through text-to-speech engine. Keywords: blind, low-vision, visually impaired, braille, screen reader, braille printer, screen magnifier, smart reader, dynamic scroll, haptic sense Cite this Article: Mohamed Raneeb, Abdul Haleem Illikkal, Aparna Krishna, Anas M., Grace Elsa George. An Assistant Reading System for Visually Impaired People. International Journal of Image Processing and Pattern Recognition. 2019; 5(2): 1–6p.
Redundant Keypoint Elimination Using Redundancy Index on Basic SIFT Algorithm
ABSTRACT Matching features across different images is a common problem in computer vision. For the images of different scales and rotations, there is the need of scale-invariant feature transform (SIFT). SIFT identifies the distinctive keypoints that are invariant to location, scale, rotation, robust to affine transformations (changes in scale, rotation, shear, and position) and changes in illumination, which are usable for object recognition. SIFT algorithm includes two main stages: feature detection and descriptor extraction. The feature detection is performed in four phases: (i) extracting scale-space extrema, (ii) improving accuracy of localization, (iii) eliminating unstable extrema, and at the end, (iv) allocation of orientation to each created feature. This project aims to identify and extract the keypoints using the SIFT algorithm based on reference image. The number of extracted keypoints is based on the complexity of image locations. Descriptor extracts the keypoints from reference image. Euclidean distances are calculated between each keypoint and all other keypoints in the reference image. Threshold value must be selected to remove redundant keypoints. The smaller distance value belongs to the more redundant keypoint and the larger distance value elimination improves the quality of the method. This project work is simulated in MATLAB 2013a for various images, and compared with the existing SIFT algorithm, optimum keypoint extraction will be performed in future. Keywords: Euclidean distance, object recognition, SIFT Cite this Article: N. Manikandaprabu, S. Vijayachitra. Redundant Keypoint Elimination Using Redundancy Index on Basic SIFT Algorithmtection. International Journal of Image Processing and Pattern Recognition. 2019; 5 (1): 1-5p.
Face Recognition and Identification Using Computer Vision
ABSTRACT In practice, identification of criminal in India is done through thumbprint identification. However, this type of identification has many constraints. Our face recognition poses to be able to detect face and recognize face automatically. Nowadays, face recognition systems use biometric for many security applications. This technique uses an image-based approach toward artificial intelligence by removing redundant data from face images through image compression using computer vision that compares a person’s face that is live capture image or a video source with the stored faces. Protecting the people from the criminals in public areas is a great challenge. Face recognition is the preliminary step for face detection. The recognized face should be exact in different viewpoints like when the countenances are turned, under complex backgrounds, lighting conditions, variety of skin tones and so forth. These ended up testing factors in the face discovery process. Numerous frameworks were proposed before for face discovery. These current frameworks guarantee face location in shading pictures or lighting conditions or foundation complexities. Some regular issues in these frameworks are that they need to experience numerous stages over and again prompting uproarious yield, less exactness, additional time utilization, and productivity levels. Hence this paper proposes a system for detection and recognition using vision assistant and classifies the input into frames for feature extraction. This system detects faces in a group of people, variability in skin tones, variability in scale, in the presence of outliners. The recognition system recognizes a suspect by comparing the face of the suspect with the faces that were stored in the database (like AADHAR). The suspect’s face is automatically forwarded to the nearby government security organization to catch the suspect. This system provides a fast detection rate accounting for better accuracy and efficiency levels. This paper provides safety to the public in crowded areas like railway stations, bus stands, cinemas theatres, market areas, shopping malls, parks and so on. Keywords: computer vision, face detection, face recognition, scaling, security Cite this Article: T. Sudharsan, S. Sumithaa, S.R. Swarga. Face Recognition and Identification Using Computer Vision. International Journal of Image Processing and Pattern Recognition. 2019; 5 (1): 6-10p.
Review on Medical Image Enhancement Techniques
Abstract—Image enhancement is the process of improving visual quality of the image. In this paper, Image Processing Methods were implemented on CTA medical images for better understanding. LPFED with different kernels, GLTLPF, are the major techniques that were used to enhance images. The output results give more insights about the images and enhance the overall scope of finding more perceptions. Keywords: Medical Image, Image Enhancement, Filter, Gray Level, Edge, Computer tomography angiography, Laplacian Filtering Edge Detection, Gray level transformation Laplacian Filtering. Cite this Article: M. Raja, S. Vijayachitra. Review on Medical Image Enhancement Techniques. International Journal of Image Processing and Pattern Recognition. 2019; 5 (1): 16-21p.
High Efficient Energy Harvesting by Solar Panel Using Polychromat Layer
Abstract:- This project is used to harvest high amount of energy by solar panel using polychromat layer. Polychromat layer is the one which splits all kinds of radiation from the sun where the other solar panels without the polychromat layer can not absorb all types of radiation from the sun. This layer absorb all type of radiation from 10^-4 to 10^-8 while the others can’t absorb it. The complete rotation in clockwise and anticlockwise direction can be controlled by using Arduino UNO microcontroller with a help of rotating motor where it gets detected by temperature sensor. It has been observed that this proposed system is capable of harvesting more energy than the other static solar panel system without polychromat layer. It can play a vital role in both domestic and for industrial purposes and so on. Apart from that we aimed to send the excess energy to the Electricity Board (EB) for society. Keywords—LDR,DCmotor,LED,L293D,Solar Cell,Voltage Regulator and PIC16F77A Cite this Article: S.P. Kesavan, C. Pradeep Kumar, N. Rishanth, C. Sivakumar, I. Sofia High-Efficient Energy Harvesting by Solar Panel Using Polychromat Layer. International Journal of Image Processing and Pattern Recognition. 2019; 5 (1): 11-15p.
Adaptive Visual Tracking for Human Motion Detection
Abstract—Object detection is one of the fundamental steps for automated video analysis in many vision applications. Object detection in video is usually performed by background subtraction techniques. In the existing method they proposed object detection by pixel variation of the image from one frame to another and the background subtracted by the training process in the recorded videos. In the proposed method the object is detected in the live video that is used for the security purpose. This method can be applicable in bank, jewellery shops, military etc., in an efficient way. Camera is fixed at the required spot and if there is any human object is recognized, it is handled and makes the framework to acknowledge and delivers the cautioning sound in the meantime gatecrasher's picture get exchanged to the comparing specialist through mail correspondence. The mailing system is composed with a counter, which sends the images of the coverage area at regular intervals. Advantages over the existing system are cost and power consumption is reduced as it does not require any sensors. Based on the Camera’s range the monitoring area may be increased. In live video 18 frame is processed at a unit time and it takes again 18 frames to process output. In existing system, they took 5secs to process 1 frame. Proposed method going to achieve 10 frames/sec. For this process MATLAB 7.12(R2011A) software is used. Keywords – Frame matching algorithm, Object Detection Cite this Article: N. Manikandaprabu, S. Vijayachitra. Adaptive Visual Tracking for Human Motion Detection. International Journal of Image Processing and Pattern Recognition. 2019; 5 (1): 1-5p.
Medical Image Fusion Using Fuzzy Logic and DHT Based Feature Extraction
Abstract The traditional medical image fusion techniques suffer from various issues such as these techniques did not detect the relevant information from the image as the whole image is considered as important whether it is meaningful to the user or not. Thus a novel fuzzy based image fusion technique is developed in this work to fuse the MRI and PET images. Before fusing the images, the 2DHT, HIS transformation and ROI is applied to the input images and then the image fusion is done on the basis of the selected ROI from the PET images. A comparison analysis of proposed and traditional image fusion techniques is driven in the terms of discrepancy value, average gradient and overall performance. After simulating the work, it is observed that the proposed work outperforms the traditional techniques. To ensure the quality of the performance of the proposed work, the analysis is also done by considering the 100 various sample images. Keywords: Image Fusion, Medical Images, 2DHT, IHS transformation, Fuzzy Inference System.
Abstract The ultrasound images are blurred by the multiplicative noise that is the Speckle pattern. It decreases the contrast and resolution of ultrasound images those outcomes in weak interpretation of image characteristics. In several image processing mechanisms like segmentation, classification and pattern recognition the speckle is a significant part. The conventional methods calculate the computation of LBP on gradient. The limitation of this technique was that the LBP mechanism divide the image into different sets of three blocks individually and in this procedure the variation of previous block did not consider while creating other block. Thus these variations affect the quality of the final images. Therefore, in this work we implements a novel approach for image despeckling to overcome the drawbacks of the traditional work. In proposed work we implement an enhanced filtration scheme after applying the LBP. The filtration is applied in proposed work to remove the noise or variations. After applying the digital filtration, its coefficients are optimized by using the differential evolutionary optimization algorithm. Keywords: Digital image, speckle noise, despeckling, LBP, Differential Evolution Algorithm, Digital Filters.
Image Denoising by Sigma Estimation based Convolution Neural Network
Abstract Image denoising process gains more attention due to its effective denoising performance. This paper proposed the convolution feedforward network with sigma estimation and without sigma estimation to provide the prior information to the CNN and without sigma, it does not come to CNN. The outcomes with CNN and without CNN use image denoising and reduce the MSE and enhance the PSNR. Keywords: Image Denoising, Deep Learning, Convolution Neural Network (CNN), sigma, DnCNN
An New Approach For Image Steganography Using LSB and BPSO Optimization
Abstract Presently, the technologies are evolving with high speed and everyday new innovations are done. As large amount of information shared between different users over the internet therefore the sharing of information is increasing day by day. In the conventional techniques of image steganography the idea of DCT and LSB approaches are used. But this technique is not optimum for securing the information. Thus, the BPSO optimization technique is proposed to determine the optimum bit or position in the data of the specific image. Benefit associated with the proposed technique is that every time the BPSO technique will determine the appropriate position to hide the information content and this is not the way as the LSB technique performs therefore the proposed technique improves the security of data as it is not an easy task for the malevolent user to find out the bits inside the encoded message in the image. The proposed technique is compared with the traditional method in terms of PSNR (Peak Signal to Noise ratio), MSE (Mean Square Error) and results obtained has shown that it is quite higher than the conventional work. Comparison is also done in terms of Entropy and the result has shown that the proposed technique is effective than the traditional work. Keywords: Image steganography, Least Significant Bit, Binary Particle Swarm Optimization, PSNR, MSE, Entropy.
Improved Approaches of Image Forgery Detection using SURF and 4-level DWT
ABSTRACT Today, there are various types of image tampering techniques, and copy-move forgery is one of them in which a piece of the picture is copied and pasted on another part of a picture. The study reviewed the several methods proposed to achieve this goal. This method generally uses both the feature point-based and the block-based algorithm. We use DWT (discrete wavelet transforms) for the decomposition of an image into several sub-images and to get the coefficients for each sub-image, the study uses4-level DWT. Considering the coefficients, this study determines the underlying size of the super pixel by SLIC algorithm to shape the non-covering sporadic squares. In order to take out the features, SURF algorithm is used to the irregular blocks. The feature is taken out from each irregular block; they are compared by calculating the Dot products between unit vectors. This gives the exactly matched tentacles of SURF feature in each block. Further, (RANSAC) algorithm is used to find the spacious area. A proposed is executed to assess the implementation of the plan the SURF calculation is utilized as a part of this study for feature extraction (FE). SURF is a productive other option to SIFT and it is substantially speedier, more hearty and give more accurate result instead of SIFT. Keywords: image forgery detection, SURF, RANSAC,4-level DWT
Artificial neural network based classification of varieties of thin and thick fabric images
Abstract: The proposed work focuses on classification of varieties of thin and thick fabric materials from images. The morphological operations such as erosion and dilation are performed. Morphological features namely, avg_moments, average_area, roundness, max_area, average_perimeter, average_eccentricity, circularity and average_equivalent_diameter are extracted. The prediction is carried out using artificial neural network. The classification rates of 84.84% and 90.90% are obtained for thin and thick fabric images. The classification rates for varieties of thin and thick fabric images are found to be 85.85% and 90.90% respectively.
Feature Extraction: Comparing Feature Extractors
Machine learning is getting fuzz nowadays a lot. In order to train the computer to recognize the set of images is the key factor. For this neural networks and classifiers are used to do the identification and recognition. Now do the matching feature points or the interest points are required which makes it to our area of research i.e. feature extractors. In our paper we compared various feature extractors Fast, Eigen, Surf, Harris, Brisk and Mser. These were compared on the basis of number of feature points extracted, strongest feature points and the region in which these features are concentrated. For this procedure, MATLAB was used.
Computing Effect of Gaussian Noise on Pixels: Testing on Red Color Triangle
Gaussian noise is the noise that effects the pixels of the image that are in the center i.e. its effect is due to change in light conditions. This paper tests the effect of gaussian noise on the red color image by graphically depicting the matrix representation of the image using mesh diagram in MATLAB. This paper proves that pixels in the center of the image are more affected when these images are used in various fields. Results of this paper have been simulated on MATLAB 2016a.
Compare Effect of Gaussian Noise on RBG Line
Gaussian noise tends to come in digital images. It enters into images when there is low light condition or high light conditions. This paper deals with comparison of gaussian noise effect on red, green, blue line image by accounting Entropy, Signal to Noise Ratio, Peak Signal to Noise Ratio and Root Mean Square Error as performance parameters. This paper proves that Blue color is the least affected and green being most affected. Results of this paper have been simulated on MATLAB 2016a.
Image Authentication Using Digital Watermarking: A Survey
Watermarking has a place with the data hiding field, has seen a ton of research intrigue as of late. Principle necessity of the Watermark is that it must be vigorous and impalpable. Heartiness of watermark as for each picture twisting can be assessed as far as effective recuperation of watermark. In the wake of recouping the watermark, the recuperated and unique watermarks are looked at by ascertaining of similitude factor of these two watermarks. In the event that the closeness factor is nearer to one than we can reason that the image is unique and additionally verified. To do as such different specimens of hued and grayscale pictures has images has taken and the performance is calculated on the basis of similarity factor. The SNR and PSNR are used to measure the nature of an image after the recreation. Here, we limit the survey to images only.
In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.[1][2] Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s).[1] When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching cubes.
: Image authentication techniques have currently added great consideration due to its importance for a large number of multimedia applications. Digital images are progressively communicated over non-secure channels such as the Internet. Therefore, military, medical and quality control images must be protected against attempts to manipulate them; such manipulations could tamper the decisions based on these images. To protect the authenticity of multimedia images, several approaches have been proposed. These methods comprise conformist cryptography, fragile and semi-fragile watermarking and digital signatures that are based on the image content. The purpose of this paper is to present evolving technique for image authentication. It also familiarizes the new concept of image content authentication and deliberates the most important requirements for an effective image authentication system design. Methods which are discussed provide strict or selective authentication, tamper detection, localization and reconstruction capabilities and robustness against different anticipated image processing operations.[
Edge detection of Image: Application of M-P-M-P Methodology
Edges of an image can be represented by using various edge detectors with different approaches. Edge detection is a very fundamental part in image processing and computer vision. In this paper, an emerging edge detection approach which computes edges of different images using the programmatic combination of Morphological-Prewitt-Morphological- Prewitt (M-S-M-S) edge detectors is presented. This approach can be used as framework for multi-scale edge detectors for a very wide range of images in the field of image processing and computer vision. Here the proposed approach is compared with each edge detector separately. Keywords---Edge detection, Computer Vision, Image Processing, Morphological-Prewitt-Morphological-Prewitt (M-P-M-P) edge detectors, Multi-scale edge detector.
Intelligent Currency Counter cum Counterfeit Detection: A Survey
Any developing country faces many challenges with respect to its economical stability. One such threat in cash transactions is fake notes. Due to advances in scanning and printing technologies the production of fake notes can be easily done. Manual detection of fake notes is time consuming and so is counting currency of different denomination in a bundle. This project focuses on building a device that accurately counts money and also detects counterfeit notes.
Upgrade Immense Region Reportage from Numerous Satellite Image Sensors
Rapid injury data assortment and dissemination throughout the disaster emergency response part could be an important remote sensing-based approach. For giant disasters like cyclone and earthquake, multiple satellite-sensor overpasses with variable inform angles square measure needed to completely cowl the massive impact space. This text presents associate in nursing improvement model for satellite image acquisition designing utilizing geographic house, time, and assortment state of affairs needs a web remote sensing designing tool model implementing the improvement model and algorithmic program is provided for disaster management agencies and emergency response call manufacturers to urge hierarchic satellite image acquisition plans.
A Study of Mobility Models in Wireless Ad-Hoc Network
The impact of mobility models depends upon the routing protocols in ad hoc networking. This work provides a comprehensive study using wide range of a wireless ad hoc network parameters, and draws conclusions for the performance of routing protocols under different mobility models. The routing protocols used are DSR. The mobility models used are Random Way Point (RWP), Gauss Markov (GM). The performance metrics used are Packet Delivery Ratio (PDR), end to end delay and throughput. The NS-2 simulation tool has been used for the study.
Communities in Complex Networks: A Review
These days we are equipped with various types of networks like social networks, biological networks, technological networks etc. These networks exist almost everywhere. Many scientists and researchers have shown their interest in these complex networks because of their huge range of applications. These complex networks have various types of properties like transitivity, scale free networks, presence of community structure, etc. Community detection is one of the most active fields in complex networks because it has many practical applications. In the paper report, we have discussed all the work done till date in the field of community detection.
Study and Analysis of Robust DCT-2L-DWT-SVD Domain Based Digital Image Watermarking Techniques with Different Attacks Using MATLAB
The proposed paper presents a combined technique of Discrete Cosine Transform (DCT), 2level Discrete Wavelet Transform (2L-DWT) and Singular Value Decomposition (SVD) based Digital Image Watermarking (DIW) with dissimilar attacks. In the initial stage, split the original (cover) image into four sub-bands LL,LH,HL,HH which are named according to the filter (high-pass or low-pass), select LL sub-band which is split in to four sub-bands - LL-LL ,LL-LH ,LL-HL, LL-HH using DWT and DCT following the SVD on every band by modifying their singular values. After obtaining the watermarked image (WI), different attacks, namely, blurring, adding noise, contrast adjustment (CA), histogram equalization (HE), swirl, median filter occur. At the end, we extract the original embedded WI in all bands and compare them on the basis of their Peak Signal Noise Ratio (PSNR), mean square error (MSE) parameters. Results obtained from (DCT-2L-DWT-SVD) showed improved performance in terms of imperceptibility, and certain aspect in robustness PSNR, and MSE than that of (DWT- SVD), in addition to the quality of recovered WI.
Facial Expression Recognition Using Voila and Jones Algorithm and Principal Component Analysis: A Propose Work
Human face detection (FD) and recognition is a tough subject matter and a lively location of research. It is not unusual in numerous fields such as image processing (IP) and pc vision. It is the number one and the first step in wide range of applications which include face recognition (FR), private identification (PI), identity verification (IV), facial expression (FE) extraction, and gender class. In this paper, a multilevel model for face detection is included based totally on Viola and Jones algorithm, Principal Component Analysis. The model showed an enhanced performance in terms of face detection rate. The purpose of this paper is to adopt and integrate some of those algorithms in order to get enhanced results.
A Real Image and the Edges Detected Using Ramp and Ridge Profile
Edge detection may be an elementary downside of laptop vision and has been wide investigated. I tend to propose a brand-new framework for edge detection supported edge profiles. Acceptable analysis functions area unit required for various kinds of edge profiles, like ramp edges, ridge edges, etc. Associate in Nursing analysis operate should meet the need that it'll manufacture native minima at the positions wherever edges of a given sort occur within the profile. Rather than subjective thresholding, image noise is deliberate statistically and used as a scientific approach of filtering false edges. This paper tends to explain our technique as “qualitative edge profile fitting” as a result of it's not supported absolute thresholding. Once a foothold purpose is localized, it is often extended into a foothold by matching compatible profiles. Two profiles area unit thought-about compatible as long as their average distinction is inside the noise measure. Another feature of our approach is its sub picture element accuracy. The use of profiles and noise-induced threshold choice build tasks like connection broken edges a lot of objective. This paper tends to develop the mandatory algorithms and execute them.
Computer-Assisted Automatic Segmentation/Detection of Multiple Sclerosis Lesions in Brain MR Images: A Review
In humans, central nervous system (CNS) may suffer with demyelinating disease, named multiple sclerosis (MS), characterized by a wide scope of neurological deficits. Magnetic resonance imaging (MRI) is an important tool for MS diagnosis which enables the detection of location and size of the affected tissue. Research on this disease is has grabbed wide attention so as to improve the treatment plans by better understanding of causes and the disease progression. The diagnostic accuracy can be improved with automatic segmentation of MS lesions. It offers a promising alternative to manual detection which is affected due to expert to expert variation, the experience of individual and is definitely a time-consuming task. The objective of this paper is to review the image processing based approaches to automate MS lesion detection in human brain MRI. This paper covers exhaustive review of different image processing algorithms or techniques used; and provide a detailed comparison of results which will enable researchers in the field, to understand the scope and challenges involved towards automatic detection and classification of MS lesions from MRI scans of brain.
Statistical Integration of Radar and Optical Data for Geomorphological Feature Extraction
Optical images of OLI sensor and radar images of the sentinel-1 were used for extraction of geomorphologic features in this study. Sentinel-1 images were acquired from two different view directions. Additionally, the three seconds SRTM elevation data was used to correct the geometric and radiometric effects of terrains. Four features of valleys, blades, fans and debris were extracted by visual interpretation. World Imagery images were used as geometric reference. Statistical parameters such as completeness, correctness, quality, and kappa coefficient were calculated for each feature. Finally, the statistical results were integrated together to achieve the highest level of reliability. The results indicate that correctness and accuracy are increased, but the quality was greatly reduced.
Detection of PARD Attack Using Key Based Biometric Authentication System Based on Fingerprint Impression
Biometric authentication plays a major role in security as these are by nature unique for every human. But the security is compromised when the pattern matching system is not accurate. Authentication system like fingerprint recognition is most commonly used biometric authentication system. In this paper, survey is done on fingerprint recognition techniques. And different approaches are studied in terms of accuracy and performance. As fingerprint may also contain noise; so, image de-noising techniques are also studied cross ridge frequency analysis of fingerprint images is performed by means of statistical measures and weighted mean phase is calculated. These different features along with ridge reliability or ridge center frequency are given as inputs to a fuzzy c-means classifier.
Analysis of Virtual or Remote Laboratories of IITs
This paper is about virtual labs challenge of Ministry of Human resource development, authorities of India. The idea of virtual laboratory is to provide a danger to perform experiments the usage of the internet and visual aids without having the equipments at their quit. The digital lab software gives a completely unique opportunity to enhance the first-rate of engineering schooling, deepen information, and provide the necessary practical competencies to young minds through price effective outreach and distance getting to know sports. digital Labs is a undertaking initiated through the Ministry of Human resource development, government of India,below the national project on training via statistics and communique generation. The undertaking ambitions to provide remote-get entry to Laboratories in diverse disciplines of science and engineering for students at all degrees from beneath-graduate to investigate. It additionally intends to broaden a whole getting to know management machine in which you can avail the various tools for studying, consisting of extra web-sources, video-lectures, animated demonstrations and self-assessment. there's also an issue in which expensive gadget and assets are shared, which are in any other case to be had to only a limited range of customers due to constraints on time and geographical distances. The challenge intends to cowl bodily sciences, chemical technological know-how and various branches of engineering like electronics and communications, computer technological know-how and engineering, electrical engineering, mechanical engineering, chemical engineering, biotechnology engineering and civil engineering.
An Efficient Unconstrained Face Recognition System Based on Multi Metric Learning
This paper is basically proposed for reconstruction-based metric learning method to learn distance metric for unconstrained face verification. As compare to other metric (only helps for label information and do not applicable for reconstruction based metric) this metric id advanced. This metric is used for multiple metric learning to remove irrelavent data for recognition.
Enhancement K-Mean Algorithm (EKM) for Image Processing in MATLAB
In this paper, a new segmentation scheme for the white blood cells from microscopic images is proposed. This is based on the K-means clustering technique. The RGB test images are converted to the L*a*b color space, and then the two color machinery (a and b) are used as features to the K-means clustering algorithm. The success of image analysis depends on segmentation reliability. The accurate partition of the image into regions is a challenging task. K-Means Clustering algorithm is the popular unsupervised clustering for dividing the images into multiple regions based on image color property. The major feature of the algorithm is that the user can specify the number of clusters- K, which is used to split the image into K regions.
Brain Waves can be used to Detect Potentially Harmful Personal Information
A researcher is working to advance research to develop secure user authentication methods, by looking at using brain waves as individual identifiers. However, those brain waves can tell more about a person than just his or her identity, warns this expert.
Using Image and Pattern Recognition to Play the Game of Connect Four Using Computer
Connect four is an advanced version of the commonly played game tic-tac-toe. Using an image recognition system, the computer can be assigned the current state of the play and using an algorithm the next move can be calculated. The extraction of image information and using it to find the solution of the diff condition is a fundamental in this field which is substantially extrapolated here. This paper is a result of our participation in a competition where we applied this concept with our robot and found quite suprising result. To play a game using a fully automated robot is a very interesting topic and this research is based on that. This entire project is done using Matlab.
Trade and Commerce Membership Facilitation for Industries and Proposed System
The project TCMF is required to create an online web form for industries to enroll themselves to register with the organization, which promotes the business of its registered members. The organization is a government of India Enterprise, which verifies the organization thoroughly before proceeding with its promotion. In addition to promotion it also facilitates many other services to its registered members. The web form will be self-explanatory to provide convenience to rural industries owners with common FAQ’s and feedback to help them filling up the form easily. The form will contain some pre-options in its attributes so to easily be marked and reduce the hassle of typing, but will also contain text boxes wherever extra description might be required to store in association with the respective industry. The membership form can also be filled by the organization online which contains all the necessary information of the organization with facility to upload proofs of documents required by respective organization to list them as their member.
To be a part of any industry like Amazon, Flipkart, etc. We need to register ourself on their portals. So, this paper demonstrates that how to generate these portals by any industry for their customers.
Designing for Fingerprint Image Enhancement
In this paper UML diagrams of fingerprint image enhancement are shown. The Unified Modeling Language is a standard visual modeling language. There are various types of UML diagrams such as Class diagram, Use Case diagram, Sequence diagram, Collaboration diagram. The data flow diagrams are also shown. The software engineering process model i.e. Waterfall model is also described
A Sequence Structure for Image Change Detection Using Sensor Network
Abstract Change detection in pictures is of nice interest due to its connectedness in several applications like video police work. This article work presents the underlying theoretical drawback of distributed image amendment detection employing a wireless detector network. The planned system consisted of multiple image sensors, which created native choices severally associate degreed send them to a foundation station through an additive white Gaussian noise channel. The bottom station then created a world call and declared whether or not a major amendment had occurred or not. This technique used four thresholds to sight native and world changes within the space being monitored. 2 thresholds outlined at the detector level helped the detector create a neighborhood call, and therefore the remaining 2 thresholds outlined at the system level helped the fusion center create a world call. Hence, by victimization four thresholds, the performance of the planned model was ascertained to own excellent fault tolerance. Keywords: change detection, qualitative sensor network, thresholds
Edge Detection of Gray-Scale Images Using P-S-B Methodology: A Novel Approach
Abstract Edge detection of images is a very serious challenge to the image processing researchers till date. Several techniques can be implemented to detect the edges of different images. But a single edge detection methodology is not enough to perform very well in each and every possible image context. In this paper, a novel edge detection methodology is presented, that computes the edges of different images using the combination of Prewitt -Sobel- Bergholm edge detectors (P-S-B). Here the edge detection of images using P-S-B technique is compared with each Operator individually. Keywords: edge detection, image context, Prewitt-Sobel-Bergholm Edge Detectors, P-S-B Technique
An Experimental Analysis Using P-S-M Approach for Edge Detection of Color Images
Abstract Edges of an image can be represented by using various edge detectors with different approaches. Edge detection is a very fundamental part in image processing and computer vision. In this paper, an emerging edge detection approach which computes edges of different images using the programmatic combination of Prewitt-Sobel-Morphological (PSM) edge detectors is presented. This approach can be used as framework for multi-scale edge detectors for a very wide range of images in the field of image processing and computer vision. Here, the proposed approach is compared with each edge detector separately. Keywords: computer vision, image processing, edge detection, multi-scale edge detector, Prewitt-Sobel-Morphological (PSM) edge detectors
Indian Lipi Recognition Using Image Analysis Technology
Abstract A document image analysis technology is optical character recognition (OCR) where scanned digital images provide as input to the system and translated into an editable machine comprehend digital text format. OCR is an interested topic for researcher in the past decades. It has been seen that efficient algorithms have increased the speed and accuracy of character recognition. A substantial amount of work has been done on foreign languages such as English, Chinese, etc. but very few papers are there for Indian languages baring a few for Hindi and Bengali. Hence, our research work was directed towards development of a novel algorithm for Offline Typewritten Odia Character recognition using Template Matching. Keywords: character recognition, digital text, image translation, OCR
Comparative Study of Classification Methods Based on Feature Reduced Microarray Gene-Expression Data
Abstract Cancer is a very complex and clinical heterogeneous disease. Now a days the research into the diagnosis and treatment of cancer have become an important issue for researchers. Cancer microarray data normally consist of samples having large number of gene expression as features. For better result in cancer classification it is very important to select relevant features. In this paper, five different types of cancer datasets are used such as colon, lymphoma, SRBCT, prostate, and leukemia, and F-Score is used for feature extraction. In my work, different classification techniques are applied on the cancer datasets. The objective of this paper is to diagnose and analyze cancer disease with the help of well-known classifiers like functional link artificial neural network (FLANN), Back-propagation Network (BPN), Kth Nearest Neighbours (KNN), and Radial Basis Function (RBF) and provide comparative study of these four classifiers in terms of their accuracy. Keywords: BPN, feature extraction, F-Score, FLANN, KNN
On Image Processing Using MATLAB: A Survey
Abstract The digital image processing research has following attributes: Image Acquisition, Image Enhancement, Image Segmentation Feature Extraction and Classification. Each steps of digital image processing research has gone long way in developing the techniques, algorithms and methods. This paper will study the use of MATLAB in the steps of digital image processing research. Record each techniques and algorithms, discuss about the sequential timeline development of the algorithms. The performance of these developments will also be studied. The algorithms discussed in this paper will be applied on the local image database. The result will be recorded. The performance analysis has been the conclusive remark. Keywords: Feature extraction, image processing, MATLAB, segmentation
Underwater Image Enhancement and Edge Detection
A hybrid image enhancement and edge detection algorithm for underwater images is proposed in this paper. The proposed enhancement algorithm augments the image quality and suppresses noise in underwater images. The novel edge detection process is centered on the pixel phase information present in images, thereby leading to edge pixels that are illumination invariant. Since, illumination influences a major portion of information in underwater images, subjective analysis of the proposed scheme with prevailing edge enhancement and object segmentation algorithms is performed and presented in this paper. The results illustrate the efficiency of the intended mechanism. Keywords: Underwater images, image enhancement, edge detection, phase congruency
Combination of Sobel-Rothwell-Bergholm Edge Detectors: An Experimental Measure for Edge Detection of Images
Edges of an image can be considered as a type of important information which can be extracted by applying several edge detectors with different techniques. Edge detection is a fundamental and crucial subject in computer vision and image processing. In this paper, a novel edge detection technique which computes edges of different images using the combination of Sobel-Rothwell-Bergholm edge detectors is presented. This method can be considered as template for multi-scale edge detectors for a wide range of images in image processing. Here the proposed methodology is compared with each edge detector separately. Keywords: Edge detection, computer vision, image processing, sobel-rothwell-bergholm edge detectors, multi-scale edge detector.
Edge Detection of Images: An Application of C-R Methodology
In this paper, a novel edge detection technique which computes edges of different images using the concept of Center of Mass (COM) with Robert Edge Detector (COM-ROBERT) methodology is presented. The proposed method can be used as framework for multi-scale edge detectors for different images in image processing. Here the edge detection by COM with Robert edge detector methodology is compared with simply COM methodology. Keywords: Edge detection, center of mass, Robert edge detector, multi-scale edge detector, image processing
Application of COM-LOG Technique for Edge Detection of Images: An Emerging Methodology
In this paper, an emerging edge detection method for computing the edges of several images using the concept of Center of Mass (COM) with Laplacian of Gaussian (LOG) Operator is presented. This method can be used as framework for multi-scale edge detectors for several images in image processing. Multi-scale edge detector needs to compute gradient on different scales. Here the edge detection by COM technique with LOG operator is compared with simply COM technique. Experimental result shows that edge detection by COM-LOG technique is compared with COM edge detection technique. Keywords: Edge detection, center of mass, log operator, multi-scale edge detectors, image processing