Journal of Image Processing & Pattern Recognition Progress
Journal of Image Processing & Pattern Recognition Progress (JoIPPRP) ISSN: 2394-1995 Click here for complete Editorial Board Scientific Journal Impact Factor (SJIF): 5.055 Journal of Image Processing & Pattern Recognition Progress (JoIPPRP) is a journal focused towards the rapid publication of fundamental research papers on all areas of Image Processing & Pattern Recognition. It's a triannual journal, started in 2014. Journal DOI no.: 10.37591/ JoIPPRP Focus and Scope Cover Image digital representation Biometrics new algorithms and/or technologies for biometrics Element of visual perception analysis of specific applications Fourier transforms, Extension to 2 - D, DCT, Walsh transform, Hadamard transforms Huffman and contour coding Restoration Models: Constrained & Unconstrained image coding processing and analysis geometric image processing and analysis document processing and recognition clustering and classification; statistical pattern recognition neural networks for pattern recognition; computer vision; video segmentation and tracking intelligent remote sensing, imagery research and discovery techniques Readership: Graduate, Postgraduate, Research Scholar, Faculties, Institutions, and in IT Companies. Indexing: The Journal is index in DRJI, Google Scholar 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@stmjournals.com or nikita@stmjournals.com Abbreviation: JoIPPRP Frequency: Three issues per year Peer Reviewed Policy Editorial Board Instructions to Authors Publisher: STM Journals A division of: Consortium eLearning Network Private Ltd Address: A-118, 1st Floor, Sector-63, Noida, Uttar Pradesh-201301, India Phone no.: 0120-4746-214/ Email: nikita@stmjournals.com
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Region Segmentation and Annotation with Vehicle Detection Validation Application in Airborne Images
In this work, the authors propose an automatic image segmentation and annotation system for airborne images. Initial region segmentation using existing region segmentation methods is applied to airborne images first. To deal with over-segmentation on the initial region segmentation results, the authors performed graph-based region merging by constructing an undirected-graph based on 8-connected local neighborhood. For each region, the authors extracted low-level features and used the Support Vector Machine (SVM) classifier to annotate the region with labels. Based on the output of the SVM classifier, adjacent regions with the same label were further merged to obtain the final segmentation and labeling result. The segmentation and annotation results are useful for vehicle detection validation. The experiments show that the proposed system can effectively segment and label various aerial images on a highly challenging dataset. Also, vehicle detection can be substantially enhanced with the help of the proposed annotation results and validation scheme.
Performance Comparison between Back-Propagation Learning and Kohonen Self-Organizing Neural Networks Algorithm in Terms of Pattern Recognition
Pattern recognition using back-propagation learning and Kohonen self-organizing neural network algorithms has been developed and measured various performance based on different criteria and environment of the pattern. These pattern recognition systems have taken the object image as input. In image pre-processing stage, scaling and clipping process has been applied from the background image to avoid unnecessary portion of the object image. Feature extraction has been performed after applying filtering and edge-detection method. The extracted feature has been used as the input of the back-propagation learning neural networks (BPN) algorithm and Kohonen self-organizing mapping (SOM) algorithm. Networks have been trained to create the knowledgebase from the input features. Finally, these learned templates have been used for testing purpose. The difference between two training procedure is that the learned weights and thresholds have been updated to calculate the output for BPN and feature mapping technique in output grid has been used for Kohonen network. Finally, the performances of both algorithms have been measured and compared the learning and recognition performance on the various selected criteria.
Back-propagation learning neural networks
Kohonen self-organizing mapping neural network
Q-Metrics for Early Detection of Cervical Cancer
The most prevalent form of cancer in women worldwide is uterine cervical cancer. Through screening programs aimed at detecting precancerous lesions most cases of cervical cancer can be prevented. In this article, Q-metrics has been proposed for carrying out automated image segmentation of uterine cervical cancer. The validation of detection of cervical lesions is an important issue in medical image processing because it has a specific impact on surgical planning. We evaluated the segmentation accuracy on the basis of a four-sample validation metric against the estimated gold standard, which was derived from several domain experts’ manual segmentations by a novel algorithm. The distribution functions of the lesion and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic (R.O.C.) curve and Dice similarity coefficient (D.S.C.) in all possible decision thresholds. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds.
Image Segmentation based on Region Merging using Breadth-First Search
This paper proposed a new method for image segmentation based on region merging using breadth-first search (BFS). The image can be partitioned into multiple segments so that meaningful information is extracted out and then image is analyzed easily. In the proposed method, first the oversegmented image is obtained by applying a standard watershed transformation on original image. Then BFS is executed on the oversegmented image to obtain a segmented image. The quality parameter F-measure has been calculated for the segmented images. The proposed algorithm is also compared with existing method and better results are obtained.
Analysis & Detection Kidney Stone Images using Fuzzy Techniques
Kidney Stone is the most debilitating sort in one of the deadliest malignancies type of tumor. Over the a few years the occasion of dangerous tumor has consistently reached out, considering the way that the fix of the disease depends upon its fundamental judgment. Two noteworthy sorts of lung tumor, Small cell & Non-small cell Kidney Stone. The lungs are normally sweeping in measure along these lines tumors can create in them for a long time before they are found. Despite when the appearances, for instance, weariness and hacking occur, people think they are a result of various causes. The approach of new ground-breaking equipment and programming strategies has activated endeavors to create PC helped symptomatic frameworks for Cancer identification in help of reasonable mass screening in creating nations. Mechanized lung division in thoracic figured tomography examines is fundamental for the advancement of computer-aided diagnostic (CAD) techniques. The accuracy is achieved up to 97.86 %
A HYBRID STEGANOGRAPHY APPROACH FOR DATA HIDING
Steganography is the strategies for concealing an information or message inside another message or cover picture without attracting any suspect to the others and that he message must be identified by its expected beneficiary. In current terms, steganography is generally executed computationally, where cover works, for example, text records, pictures, sound documents, and video documents are changed so that a mysterious message can be inserted inside them. In this paper JSteg and JMQT techniques and their encoding and deciphering calculation has been talked about exhaustively. Besides , exhibitions grids for steganography has been talked about exhaustively. An examination is made between three boundaries namely, PSNR, Capacity and stego Size utilizing two steganography strategy to be specific; JSteg and JMQT has been made. The test results reason that utilizing the shading part method for shading picture steganography JMQT gives great PSNR and limit when contrasted with JSteg and JSteg gives great stego size proportion results as contrast with JMQT. In segment first we need to examine about presentation
A Review on Different Techniques Used for Feature Extractions from Mammogram of Breast Cancer
Breast cancer detection is still a complex and challenging problem. Mammogram contains low signal-to-noise ratio (SNR) and a complex structured background so identifying cancer tissues using this technique is a time consuming task even for expert radiologists. Therefore, there is still a need to enhance imaging in digital mammogram, where enhancement in medical imaging is the use of computers to make the image clearer. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively slow and inaccurate in some cases. Providing automatic, fast, and accurate image processing and artificial intelligence-based solutions for that task can be of great realistic significance. This paper discusses about different techniques used to scan the whole mammogram and perform filtering, segmentation, and features extraction.
Comparative Study on Methods for Teeth Image Recognition
This article proposes a new method to find a specific tooth from the digital picture of multiple teeth. The modified methods can significantly simplify the pattern recognition process for teeth. Here we compared some of the popular recognition method like principal component analysis (PCA) and hybrid differential method (HDM). These methods are lot more simple, runs faster and the identification rate is better. These approaches will reduce the traditional reliance on X-ray image to get information and make dental decision. It can be used in the dental decision making software to do preliminary dental advising for potential patients.
A Survey on Face Recognition Techniques and Its Applications
Face recognition has been a fast growing, and interesting area in real time applications. Automatic face recognition has shown great achievement for high-quality images under embarrassed conditions; for video-based recognition it is hard to achieve similar levels of performance. Human face detection and tracking of moving objects in the video are important tasks of the computer vision. Human and object frames are separated by implementing a face detection algorithm for the video. In this paper we have discussed, analysed & compared some popular face detection methods like PCA, ICA, LDA, PCA + LDA hybridization and Gabor wavelet transformations. Here we have tried to attempt a comparative study of these methods.
Fisherfaces Linear Discriminant Analysis
Extracting Alphabets and Numbers from License Plate using Mean Value
Segmentation plays a significant role in computer vision and its related applications of image processing. Segmentation is actually a process of partitioning a digital image into multiple segments (sets of pixels) to simplify and/or change the representation of an image into more meaningful and easier to analyze. This paper presents a method of license plate extraction using segmentation method and mean value of the images. We extracted the numbers and characters from the license plate by calculating the common mean value of each character. The method proved as simple, fast and accurate for license plate extraction.
A Novel Approach for Texture Feature Extraction to Retrieve Texture Images using Gray-Level Co-occurrence Matrix (GLCM)
Image retrieval plays an important role in many of the areas. Content based image retrieval (CBIR) is used for browsing most similar images from the large database. Texture is an important characteristics used in identifying the region of interest or objects in an image. In this image retrieval method, texture method is used to extract the features and retrieves the similar images based on similarity of the features. In the texture method, gray-level co-occurrence matrix (GLCM) texture method is used to extract the texture features. After extracting, the features are compared with the database images and distance is calculated using the Euclidian distance. Finally, similar images are retrieved according to the user satisfaction. Performance of the system is calculated using the processing time, execution time, precision and recall rate metrics.
Improved Global Region based Chan-Vese Model
Chan-Vese model is one of the fundamental active contour models for image segmentation. In this paper, the authors propose an improved global region based Chan-Vese model which provides solution to some limitations of basic Chan-Vese model. The proposed model avoids re-initialization requirement to increase speed of algorithm by reaction-diffusion method. The stopping criterion to stop segmentation process is also added in the model. To make model automatic, initialization free feature is also added via Poisson’s inverse gradient technique. Experimental results on different images justify the improvement in the basic Chan-Vese model.
Performance Evaluation of Segmentation Algorithms for Apple Fruit Grading
For supplying high-quality food products within a short time, automated grading of fruits is getting special attention. Fruit grading technique will grade the fruits using their shapes, color and outer look. This emphasizes the necessity of image segmentation which extracts important features from an image. This paper consists of analysis of different segmentation algorithms for detecting damage part of fruit. Marker-based watershed algorithm uses internal and external markers to locate catchment basins which are regions of interest. Quadtree and merge algorithm divides an image within a complete tree representation and then merges the regions based on some criteria. Seed region growing algorithm selects the seed point and grows the region according to pre-defined criteria. The comparison of the present approach in terms of the measures like energy, discrete entropy, relative entropy, mutual information, normalized mutual information and redundancy is also carried out.
A Machine Vision Approach for Grading of Onions
Grading of fruits and vegetables (on the basis of various external and internal quality attributes) is an important unit operation in the food industry. For products such as onions, grading is performed mainly on the basis of size. In the present study, a machine vision system was used to grade onions into three categories (extra-large, medium and small) based on variations in size. The machine vision system consisted of a CCD camera, illumination source, holding unit, sample platform, and computer. The geometric mean diameter of the sample was used as the classification parameter. It was observed that the system gave an overall classification efficiency of 93% with better results for extra-large grade. Similar approaches can be successfully implemented for classification of other agricultural products using various grading parameters.
Data Embedding Technique for Palette Scale Image using Derived Genetic Algorithm (DEP-DGA)
Steganography method is used for exchanging the hidden information. It is defined as the study of hidden communication that deals with the existence of communicated messages. The existing methodology for minimizing additive distortion in steganography fails in measuring the level of distortion for the given source palette digital image. Perfect sphere shaped palette digital images are not suitable for selection, as cover models because the hidden data might be easily observed even after specific modification by embedding method. To shape constraints in palette digital images and to observe the distortion, data embedding technique using derived genetic algorithm for palette scale image has been proposed. Row major order is used to embed huge quantity of stego message on source palette digital image. Four bits of data hiding message is embedded per byte of the source palette digital image onto the rightmost 4 bit of each pixel. Mutation is applied on the embedded palette digital image. In the process of embedding, dimension of the data hiding message is followed by the content of the original palette based digital image. Reverse procedure is followed during decoding. Derived Genetic algorithm is used to enhance the security level without any extraction of data by attackers. Several statistical parameters computed are compared with the existing additive distortion syndrome trellis code method using Statlog Dataset from UCI repository. DEP-DGA approach obtained better results in terms of security level of hidden information, 3.50 % improved throughput on using the palette based digital image for embedding and minimize the RMS error between the stego message and the palette based original image.
Multi-Modal (Hybrid) More Efficient and Highly Secured Enhanced Hand Geometry Authentication using 3 Steps Authentication by Means of Biometrics, Steganography and Encryption
“Biometrics” is the science, which is used to verify the identity of the persons through either behavioral traits or physical characteristics. This area has gained great importance in maintaining the security of the places that require high security accuracy. Hand geometry is considered one of the biometrics, which derived its reputation from the ease of use and the acceptance of many people to use it. Hand geometry based biometric systems are gaining acceptance in low to medium security applications. Hand biometrics is extensively used for personal authentication. The widespread dissemination of verification systems using biometrics and continuous attempts to break the security of these systems, such as the use of fingerprints rubber to break the security systems that use fingerprint, or use a voice recorder to break the security systems that use voice tag, and so on. Although the efficiency of biometrics in identification of people is very accurate and highly secured, but still the data concerning it is not a secret, and if it is compromised, it would compromise the integrity of the system where protection is required. Protecting biometric data has become an important issue, so that it cannot be misused by attackers. In order to increase security of biometric data there are different methods in which digital steganography could be widely accepted. Steganography is defined as the science of hiding or embedding information in a transmission medium in a way to be undetectable by observers. To address these issues, this paper proposes a novel encryption method with password protection based on an enhanced version of multi-modal hand geometry authentication using three authentication layers based on hand geometry verification, digital steganography, and password encryption.
A Novel Approach to Edge Detection and Performance Measurebased on the Theory of “Range” and “Bowley’s Measure ofSkewness” in a Noisy Environment
We proposed a new edge detection algorithm in a salt and pepper noisy environment without any pre-processing. The algorithm is a simple, unique mathematical expression. It is the product of three terms: median, range and some edge image enhancement constant. The algorithm assumes that the edge exists in an image at non-zero skewness. We also proposed a performance measure technique by using “Bowley's measure of Skewness”. The coefficient of Skewness due to Bowleylies between–1 and +1. We measured the absolute value of the coefficient of Skewness. The effectiveness of the above edge detection algorithm as well as performance measure technique were experimented and compared with other existing techniques. In all the experiments, our method demonstrates comparatively better results in the Salt and Pepper noisy environment as well as in the noiseless image.
Artificial Intelligence Powered Computer
Gesture control along with Artificial Intelligence has been very interesting topic in image processing for a long time. This is because it is very difficult for a computer to differentiate between background and foreground data in a given image. These technologies enable a person who has zero interaction with computer to use the various computer technologies like controlling cursor and certain functions of various computer applications. In this project we explore the possibility of controlling various applications within a laptop using hand gestures. Rather than utilizing a console, mouse or joystick, we can utilize our hand motions to control certain elements of a PC like play/stop a video, move left/directly in a photo slide show or scroll up/down in a web page and many more. These gestures will be detected by the laptop using its webcam only. The technologies used in this project are Python, Image Processing and Artificial Intelligence algorithms.
External Filtering and Wavelet Domain Thresholding-based Denoising Method for AWGN corrupted imagesExternal Filtering and Wavelet Domain Thresholding-based Denoising Method for AWGN corrupted images
In this work an image de-noising method with external bilateral filtering and wavelet domain thresholding has been proposed. In Gaussian filtering, it fails to denoise an image at edges where the spatial variations are not smooth and causesblurs at the edges in the image. External bilateral filter (EBF) beats this by filtering the picture in both reach and area (space). Respective separating is a nearby, nonlinear and non-iterative procedure which thinks about both dim level (shading) likenesses and mathematical closeness of the adjoining pixels. With bilateral filter, the approximation sub-band results in loss of some image details, whereas that after each level of wavelet reconstruction flattens the gray levels causing anunpleasing output image. To overcome the above issue, extension of bilateral filtering with introduction of wavelets for thresholding has been proposed. Instead of direct filtering or direct wavelet domainthresholding of noisy image, the proposed method first obtains the filtered version of image using bilateral filtering and then this filtered version of image undergoes to wavelet domain thresholding using Bayes-shrink rules. In this approach the advantages of both the methods are achieved. To check the effectiveness of the proposed method in image denoising, we have compared the results with recent image denoising methods.
Study and Analysis ofMATLAB-based Face Masks Recognition System
Due to the corona epidemic, numerous kinds of face covers are being utilized. Use in face covers is the solitary material that mirrors the nature of the veil. The objective of the present study is creating an auto face recognition system through face mask recognition system through MATLAB to protect people from the corona epidemic. As the human face mask is a dynamic object having a high degree of variability in its appearance, it makes face mask detection a difficult problem in computer vision. Accuracy and speed of identification is the main issue. A mask acknowledgment framework might be a PC application for naturally distinguishing or checking a person from an advanced picture or a video outline from a video source. The expanding commonness of irresistible infections in ongoing many years has represented a critical danger to general wellbeing. The respiratory droplet or airborne route has the best potential to disrupt intercourse while being amenable to prevention by the standard facemask. According to the World Health Organization (WHO), people showing no signs or symptoms of COVID-19 can transmit the virus to others.
Survey on Techniques to Segment Salt Bodies in Seismic Images
The areas in the earth which are rich in oil and natural gas also have huge deposit of salt below the surface. So, it is very important for the hydrocarbon industries to acquire the needed resources. The important challenge of seismic imaging is to detect subsurface salt structure which is required for the identification of hydrocarbon reservoirs and drill path planning. Unfortunately, the accurate identification and classification of large salt deposits is very difficult because of its non-linearity, and seismic imaging techniques often require professionals to interpret the salt bodies. In several fields, Convolutional neural networks (CNNs) have been successfully applied, and have got accurate results. Because of the less availability of labeled data, the performance of the CNN has been reduced.Seismic image analysis on a huge volume of data requires more labor and it is a time-consuming task if performed manually. In the past years, several efforts have been made to automate or semi-automate the process of identifying the salt bodies. In addition, it requires experts for identifying the right geological features. Machine-learning algorithms have also been used in the past which performed better than the existing methods. Recently, deep learning methods have been employed in this area, which give much better results compared to machine learning algorithms. So, the most important thing is to identify the accurate method for this segmentation problem. In this study, several deep learning models has been described so that it will be helpful to identify the perfect method which will lead to accurate result compared to the alreadyavailable methods. In this study, several network models have been described which will help to chose appropriate method for this segmentation problem.
An Analysis on Gender Classification using Face Images Using DWT, (2D)2PCA and SVM
Abstract Classifying gender compared to others and is used in this work. Biometry is the use of physical characteristics like face, fingerprints, iris, etc. of an individual for personal identification. Some of the difficult issues of face biometrics are face detection, face recognition, and face identification. These problems are being researched by the computer vision community for the last few decades. Considering the big population, the authentication process of an individual usually consumes a significant amount of time. One of the attainable solutions is to divide the population into two halves supported gender. This will facilitate to scale back the search area of authentication to nearly half the present information and save substantial quantity of your time. Gender identification through face demands use of sturdy discriminative options and strong classifiers to separate the feminine and male faces with no ambiguity. In this thesis, an investigation has been created on gender classification through facial pictures mistreatment principal part analysis ((2D)2PCA), and support vector machine (SVM). Principle Component Analysis (PCA) could be a spatial property reduction technique, which is used to represent each image as a feature vector in a low dimensional subspace. SVM could be a binary classifier that PCA is the input within the style of options and predicts which of the two attainable categories forms the output. Initially face region is extracted employing a planned complexion segmentation approach. The face region is then subjected to PCA for feature extraction that encodes second order statistics of information. These principal elements area unit fed as input to SVM for classification. Keywords: Biometrics, face detection, gender classification, (2D)2PCA, SVM
Detect Fraud Bank Checks with Convolutional Neural Network Processing Algorithm
Abstract The purpose of this study is to design and build a fraud check detection system with a Convolutional Neural Network (CNN) algorithm to quickly and easily detect a fraud or altered bank check in real time. A MATLAB Deep Learning Toolbox with related CNN algorithm is used to assist this study to quickly detect any fraud or altered bank check when it is deposited and scanned by a scanner in any ATM in US. The testing and validation processes have been performed to confirm the effectiveness and correctness of this detecting system. The current correct detecting rate is 97.5% for checks deposited via ATMs. Keywords: Altered check inspections, convolutional neural network processing, fraud bank check detections, image signal processing, image detections
Visual Features based Paddy Leaf Disease Recognition, its Severity Detection and Remedy Prediction using K-means Clustering and AdaBoost
Abstract Agriculture is a vital part of an economy. Paddy is one of the main food crops which play a major role in agricultural field. The gross national income of a country depends on paddy cultivation. But the production of paddy is damaged due to different types of paddy leaf diseases. Generally, farmers and agricultural experts identify diseases manually which is very ineffective and time consuming. So effective recognition, severity detection, and proper management of paddy leaf diseases are necessary. This paper presents visual feature based recognition system for three common paddy leaf diseases (Brown Spot, Leaf Blast, and Bacterial Leaf Blight) using K-means clustering and AdaBoost classifier. To separate the affected cluster from diseased leaf, K-means clustering is used, and the affected ratio is calculated to detect the severity of diseases. The classification of diseases is performed using color and texture feature analysis. Mean and standard deviation are used as color feature. For texture feature extraction, correlation and wavelet packet entropy are used. AdaBoost classifier is applied to recognize the type of diseases. After recognizing the diseases, necessary remedy is suggested so that farmers can take necessary measures for management of paddy leaf. The proposed system shows comparatively robust result. Keywords:AdaBoost, affected ratio, color moments, correlation, K-means clustering, remedy, wavelet packet entropy
Automated Land Crude Oil Spill Detection with Gabor Filters and Wavelet Transform
Abstract Early detection of crude oil spills leads to significant reduction in the environmental pollution, economic losses and health hazards that result from these spills. The visiblelightspectrumstill remainsaresearcharea for spill detectionbecauseitis aquick economicalmethodformonitoringoil spills. This paper presents an algorithm for automated land crude oil spill detection in the visible spectrum. The algorithm employs three basic steps for crude oil spill detection: Gaborfiltration, wavelet transformanalysis, and color homogeneity extraction. The algorithm was tested on sixty (60) ground truth crude oil spill images obtained from the Crude Oil Spill Imaging Database (COSID). The algorithm was able to detect the crude oil spill in 55 of the 60 images, resulting in an efficiency of 91.7%.It was observed that in certain cases, the algorithm incorrectly flagged regions of vegetation as crude oil. The algorithm, therefore, requires the integration of a vegetation segmentation and removal step to reduce the number of false positives detection. Keywords: Color homogeneity, crude oil spill detection, haar filters, Gaborfilters, wavelet analysis