Journal of Image Processing & Pattern Recognition Progress
https://computers.stmjournals.com/index.php?journal=JoIPPRP
<p align="center"><strong>Journal of Image Processing & Pattern Recognition Progress (JoIPPRP)</strong></p><p align="center"><strong> </strong></p><p align="center"><strong>ISSN: 2394-1995</strong></p><p align="center"> </p><p align="center">Click <strong><a href="/index.php?journal=JoIPPRP&page=about&op=editorialTeam">here</a> </strong>for complete Editorial Board</p><p align="center"> </p><p align="center"><strong><br /></strong></p><p align="center"><strong>Scientific Journal Impact Factor (SJIF):</strong> 5.055</p><p align="center"> </p><p><strong><br /></strong></p><p><strong>Journal of Image Processing & Pattern Recognition Progress (JoIPPRP)</strong> 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.</p><p align="center"><strong> </strong></p><p><strong> </strong></p><p><strong> </strong></p><p><strong>Journal DOI no</strong>.: 10.37591/ JoIPPRP</p><p><strong> </strong></p><p><strong>Focus and Scope Cover</strong></p><ul><li>Image digital representation</li><li>Biometrics</li><li>new algorithms and/or technologies for biometrics</li><li>Element of visual perception</li><li>analysis of specific applications</li><li>Fourier transforms, Extension to 2 - D, DCT, Walsh transform, Hadamard transforms</li><li>Huffman and contour coding</li><li>Restoration Models: Constrained & Unconstrained</li><li>image coding</li><li>processing and analysis</li><li>geometric image processing and analysis</li><li>document processing and recognition</li><li>clustering and classification; statistical pattern recognition</li><li>neural networks for pattern recognition; computer vision; video segmentation and tracking</li><li>intelligent remote sensing, imagery research and discovery techniques</li></ul><p><strong> </strong></p><p><strong> </strong></p><p><strong>Readership:</strong> Graduate, Postgraduate, Research Scholar, Faculties, Institutions, and in IT Companies.</p><p><strong> </strong></p><p><strong> </strong></p><p><strong>Indexing: </strong>The Journal is index in DRJI, Google Scholar</p><p> </p><p> </p><p> </p><p><strong>Submission of Paper: </strong></p><p><strong> </strong></p><p>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.</p><p> </p><p>Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration.</p><p> </p><p>Papers are accepted for editorial consideration through email info@stmjournals.com or nikita@stmjournals.com</p><p><strong> </strong></p><p><br /> <strong>Abbreviation: </strong><strong>JoIPPRP</strong><em></em></p><p><em><br /> <br /> </em><strong></strong></p><p><strong> </strong></p><p><strong>Frequency</strong>: Three issues per year</p><p> </p><p><strong><a href="/index.php?journal=JoIPPRP&page=about&op=editorialPolicies#peerReviewProcess">Peer Reviewed Policy</a></strong></p><p><strong><span style="text-decoration: underline;"><br /></span></strong></p><p><strong><span style="text-decoration: underline;"><a href="/index.php?journal=JoIPPRP&page=about&op=editorialTeam">Editorial Board</a></span></strong><strong></strong></p><p> </p><p> </p><p><strong><a href="http://stmjournals.com/pdf/Author-Guidelines-stmjournals.pdf">Instructions to Authors</a></strong></p><p><strong>Publisher:</strong> STM Journals A division of: Consortium eLearning Network Private Ltd</p><p><strong>Address:</strong> A-118, 1<sup>st</sup> Floor, Sector-63, Noida, Uttar Pradesh-201301, India</p><p><strong>Phone no.:</strong> 0120-4746-214/ Email: nikita@stmjournals.com</p><p> </p>en-USJournal of Image Processing & Pattern Recognition Progress2394-1995<p align="center"><strong>Declaration and Copyright Transfer Form</strong></p><p align="center">(to be completed by authors)</p><p>I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is <span>not</span> published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is <strong><span>not</span></strong> under consideration of publication elsewhere.</p><ul><li>I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.</li><li>I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).</li><li>I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.</li><li> I/We also agree to the authorship of the article in the following order:</li></ul><p>Author’s name </p><p> </p><p>1. ________________</p><p>2. ________________</p><p>3. ________________</p><p>4. ________________</p><table width="100%" border="0" cellpadding="0"><tbody><tr><td valign="top" width="5%"><p align="center"> </p></td><td valign="top" width="95%"><p>We Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.</p></td></tr></tbody></table>A HYBRID STEGANOGRAPHY APPROACH FOR DATA HIDING
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2792
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 presentationShifali DuaFaisal *2021-08-252021-08-258Analysis & Detection Kidney Stone Images using Fuzzy Techniques
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2785
<p>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 %</p>Sachin KumarShalini Dev2021-08-072021-08-078A Novel Approach to Edge Detection and Performance Measurebased on the Theory of “Range” and “Bowley’s Measure ofSkewness” in a Noisy Environment
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2641
<p class="Standard"><em>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.</em></p>Ratnesh KumarKalyani Mali2021-06-122021-06-128Survey on Techniques to Segment Salt Bodies in Seismic Images
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2687
<p><em>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.</em></p>Amrutha RoseT Rajasenbagam2021-06-122021-06-128Study and Analysis ofMATLAB-based Face Masks Recognition System
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2688
<p><em>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</em><em>. 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.</em></p>SARSWATI CHOUDHARYSILKY PAREYANI2021-06-122021-06-128External Filtering and Wavelet Domain Thresholding-based Denoising Method for AWGN corrupted imagesExternal Filtering and Wavelet Domain Thresholding-based Denoising Method for AWGN corrupted images
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2686
<p><em>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. </em></p><p> </p>Sumit Singh PariharShailesh Khaparkar2021-06-122021-06-128Artificial Intelligence Powered Computer
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2685
<p class="Abstract"><em>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.</em></p>Tejas RamekarOmkar SutarShrey SharmaBabita Bhagat2021-06-122021-06-128Visual Features based Paddy Leaf Disease Recognition, its Severity Detection and Remedy Prediction using K-means Clustering and AdaBoost
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2592
<p align="center"><strong><em>Abstract</em></strong><em></em></p><p><em>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.</em></p><p><em> </em></p><p><strong><em>Keywords:</em></strong><em>AdaBoost, affected ratio, color moments, correlation, K-means clustering, remedy, wavelet packet entropy</em></p><p><em> </em></p>Farhana Tazmim PinkiNipa KhatunS. M. Mohidul Islam2021-01-092021-01-098An Analysis on Gender Classification using Face Images Using DWT, (2D)2PCA and SVM
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2602
<p><strong> </strong></p><p align="center"><strong><em>Abstract</em></strong><em></em></p><p><em>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)<sup>2</sup>PCA), 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.</em></p><p><em> </em></p><p><strong><em>Keywords:</em></strong><em> Biometrics, face detection, gender classification, (2D)<sup>2</sup>PCA, SVM</em></p>Jyoti ThakurPreeti Rai2021-01-092021-01-098Detect Fraud Bank Checks with Convolutional Neural Network Processing Algorithm
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2587
<p align="center"><strong><em>Abstract</em></strong><em></em></p><p><em>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.</em></p><p><em> </em></p><p><strong><em>Keywords:</em></strong><em> Altered check inspections, convolutional neural network processing, fraud bank check detections, image signal processing, image detections</em></p>Ying BaiDali Wang2021-01-092021-01-098Automated Land Crude Oil Spill Detection with Gabor Filters and Wavelet Transform
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2601
<p align="center"><strong><em>Abstract</em></strong></p><p><em>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. </em></p><p><em> </em></p><p><strong><em>Keywords: </em></strong><em>Color homogeneity, crude oil spill detection, haar filters, Gaborfilters, wavelet analysis<strong></strong></em></p><p> </p>Donatus Uchechukwu OnyishiO'tega Ejofodomi2021-01-092021-01-098Bayesian Image Segmentation using HMRF Algorithm
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2600
<p align="center"><strong><em>Abstract</em></strong></p><p><em>Image segmentation is a process of dividing the image in to some distinct regions. These regions shave specially coherent in nature and have similar attributes. This technique is widely used for image analyses and to interpret the desired feature. In this present paper, we will study about the hidden Markov random fields (HMRF) and find its expectation maximization algorithm. The main idea behind developing HMRF is to adjoin the “data faithfulness” and “model smoothness” that show very similar nature with the active contours, GVF, graph cuts, and random walks. Here we also use the HMRF-EM along with the Gaussian mixture models, and then we use it on color image segmentation process. These algorithms are implemented in MATLAB. In color image segmentation experiments, we observe that the result obtain from HMRF segmentation are much smoother then the direct k-means clustering. The segmented object is much closer to the original shape than clustering. The segmentation time for Bacteria 1, Bacteria 2, SAR and brain images are 0.35, 0.43, 0.12 and 0.12, respectively. The accuracy for Bacteria, Bacteria 2, SAR and brain images are 97.70, 98.06, 98.89 and 97.35%, respectively.</em></p><p><em> </em></p><p><strong><em>Keywords:</em></strong><em> Bayesian methods, convex optimization, image segmentation, spatial mixture models, Potts Markov random field</em></p><p align="center">.</p>Pooja PanchalShyog Sharma2020-12-292020-12-298Semantic Classification of Images in Hierarchical Manner using Fuzzy Rules and HSVM Classifier
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2421
<p>Hierarchical structure classification is more effective in concept based classification. The feature vector is varying with the different concepts, though it belongs to the same class. Images which are similar in appearance are not correlated semantically. Hence an author made an attempt to classify the images in the dataset in concept wise in a hierarchical manner. For this, an author manually clustered the same concept images in a layer by layer to explore the semantic correlation of the concepts. A number of nodes in the hierarchy tree represent the number of classes in the dataset. Each node is classified into sub-nodes by the fuzzy classifier using fuzzy association rules. Fuzzy inference system trained by the fuzzy rules base, which is generated by the fuzzy feature vector. The performance of the fuzzy classifier is validated with multiclass support vector machine classifier and hierarchical support vector machine classifier. The dataset used here is COREL dataset; an author selected 8 concepts for the classification.</p><p>Keywords: Fuzzy classifier, fuzzy rule, hierarchical SVM, multiclass SVM, semantic concepts</p><p>Cite this Article: Thirumala Lakshmi K, Usha Kingsly Devi K. Semantic Classification of Images in Hierarchical Manner using Fuzzy Rules and HSVM Classifier. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(2): 33–54p.</p>Thirumala Lakshmi2020-08-122020-08-128Evaluating Facial Attractiveness through Proportions Analysis Based on Geometric Features
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2186
<p>Nowadays, having information about features of an attractive individual’s face plays a crucial role in self-confidence and high performance in such careers as managing a company, marketing, modeling, advertising, etc. In this paper, we intend to determine the features of an ideal individual’s face. For this purpose, we used our proposed method in addition to four angles and distance analysis to find out more parameters of an ideal face. In our method new points (left mouth corner, outer corner of the left eye, right mouth corner, outer corner of the left eye,) and new distances are defined for analyzing individual faces. After that, the results from all the methods mentioned above are compared in both cases of normal attractive individuals. MATLAB software was used to compute all parameters of each considered methods. Our results can be used as criteria to discriminate attractive and normal individuals. Our methods can be used in such applications as automatic face image filtering systems modeling and fashion show, business, and marketing.</p><p>Keywords: Facial Attractiveness; Geometric Feature; Facial Beauty; Attractiveness Assessment; Statistical Analysis</p><p>Cite this Article: Shakiba Ahmadimehr, Mohammad Karimi Moridani. Evaluating Facial Attractiveness through Proportions Analysis Based on Geometric Features. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(2): 20–26p.</p>Shakiba AhmadimehrMohammad Karimi Moridani2020-08-122020-08-128Enhancing Radiographic Dental Image Visualisation with Colourisation and Graphical User
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2471
<p>Radiography has been used in dentistry to find cavities, bone loss, hidden dental structures, tumour, and cysts. Due to the monochromatic characteristic of X-ray images, it is difficult to discern disorders and explain the diagnosis procedure. Enhance the visualization of X-rays is one of the ways to ease the work of the dentist. There were several studies to enhance visualization by giving pseudo colour shades to monochromatic images. The conclusion of these Studies has fixed shades of colour. The present research concentrates density slice approach for colorization and GUI (Graphical User Interface) to provide all information on a single window. The density slice technique is based on the intensity of pixels. This approach helps to select regions according to the intensity of pixels. Due to that, region-based Colourisation is possible. Each region represents different tooth parts. Along with this, the study also contained Graphical User Interface contained all requires images in one window so the dentist can easily explain. The study concludes that the outcomes of preset study help dentists into diagnosis. An output of this methodology is the polychromatic image and classifies region of the tooth in different colour and GUI provides better flexibility to the dentist to apply and explain the disease.</p><p>Keywords: medical image processing, colorization, visualization, dentistry, colour X-ray image</p><p>Cite this Article: Karan Patel, Bhavesh Parmar. Enhancing Radiographic Dental Image Visualisation with Colourisation and Graphical User Interface. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(2): 7–19p.</p>Karan PatelBhavesh Parmar2020-08-122020-08-128Fatigue Detection using Artificial Intelligence to Prevent Accident
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2461
<p>Each year many people lose their lives due to road accidents around the world. Major accidents are due to drowsy driving. Fatigue and micro sleep of the driver are often the root cause of serious accidents. However, initial signs of tiredness can be detected before a critical situation arises. The method used to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors. Therefore, in this paper, light-weight, real-time driver’s drowsiness detection system is developed and implemented on raspberry pi. The system capture the live video and detects driver’s face in every frame by employing image processing techniques. The system is able to detect facial landmarks, computes Eye Aspect Ratio (EAR) and Eye Closure Ratio (ECR) to detect driver’s drowsiness based on adaptive thresholding. Machine learning algorithms have been used for better approach.</p><p>Keywords: Drowsiness detection, Facial Landmark Detection, EAR (Eye Aspect Ratio), ECR (Eye Closure Ratio), Fatigue Detection, Non-Intrusive Methods, Driver monitoring system.</p><p>Cite this Article: S.Kiruthiga, S. Sharukhan, R. Mugunthan, B. Mukesh Kumar. Fatigue Detection using Artificial Intelligence to Prevent Accident. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(2): 1–6p.</p>S. KiruthigaS. SharukhanR. MugunthanB. Mukesh Kumar2020-08-122020-08-128Image Processing based Wall Crack Detection
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2527
<p>Concrete spalls and crack detection of structures may be a labour intensive and daily task. But, it plays a crucial role in health monitoring of civil infrastructures and buildings. Automated inspection with automated models has been considered one among the simplest ways to eliminate both error and price . This paper presents an automated approach using Camera and towards a Concrete Structure Spalling and Crack database (CSSC)[A], which is far and away the primary released database for deep learning inspection. We aim locate the spalling and crack regions to help 3D registration and visualization. For deep inspection, we offer an entire procedure of knowledge searching, labeling, training, and post processing. We further present a visible Simultaneously Localization and Mapping(SLAM) approach for localization and reconstruction. From comparative experiments and field tests, it is possible to achieve upto 70% accuracy using CSSC database.</p><p>Keywords: image processing, crack detection, ccny, machine learning, technology</p>Cite this Article: Ranpise Mahesh, Pawar Abhijit, Raskar Akshay, Mahi K. Image Processing based Wall Crack Detection. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(2): 27–32p.Ranpise MaheshPawar AbhijitRaskar Akshay2020-08-062020-08-068Classification of Mammogram Images Using Multi-SVM
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2379
<p>Bosom malignant growth is normal in ladies these days. It first starts when cells in the bosom start to develop wild. These cells for the most part structure a tumor that will frequently be seen on an x-beam or felt as an irregularity. Cells in about any piece of the body can become malignant in growth and can spread to different regions of the body. In the current framework, rule based methodology is utilized in order which gives a static range an incentive for various classes. Along these lines we won't capable, powerful pictures or exception conduct pictures. Highlights set is not standardized. Thusly various highlights show various yields and show diverse portrayal during preparing period of classifiers. Classifiers are not ready to recognize include covering. Hence at the learning stage, an example of the picture is not recognized. The multi - characterization issue is not streamlined and disregard the class unevenness issue. In this way, in adapting all classes is not contributing to learning stage so it turned into a one-sided learning. The proposed methodology first starts when cells in the chest begin to create insane. These cells by and large structure a tumor that will normally be seen on an x-shaft or felt as a projection. Cells in about any bit of the body can advance towards turning out to be a threat and can spread to various zones of the body. There are just around six periods of chest threat. It is always found that the revelation of threatening development at the chief stage can fix it. A model picture is taken as an information and differentiated and the photos recently set away in the database related to harm. If the disclosure is found productive, by then, relating treatment is proposed. The period of harm is being appeared and separate treatment is being urged to the patient. Stage keen treatment and prescriptions are given to fix that dangerous development.</p><p>Keywords: Fuzzy C means clustering, Gaussian filter, GLCM, Haar wavelet transform, image scaling, morphological, region of interest, statistical features, K-implies clustering, multi-SVM (Support Vector Machine)</p><p>Cite this Article Anbumani A., Suresh Kumar P., Sathishkumar A. Classification of Mammogram Images Using Multi-SVM. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(1): 32–51p.</p>A. AnbumaniSuresh Kumar P.Sathishkumar A.2020-05-202020-05-208Quality Inspection, Maturity Detection, and Size based Grading of Various Types of Mangoes using Machine Learning Methods
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2376
<p>Mango is the most significant and flavorsome fruit in most continents, especially in Asia. Mango grading is an important task in agro-industry. The grading process creates many problems during harvesting for mango growers. The manual grading process is performed by visual inspection and it is very time consuming and labor intensive. Due to different market prices and different market demand, automation in mango grading plays an important role to achieve better accuracy and consistency. In this study, an automatic mango grading system is developed using machine learning and image processing techniques. The system is divided into four phases. In the first phase, quality inspection of mango is performed using Convolution Neural Network (CNN) to detect healthy and diseased mango. In the second phase, different types of healthy mangoes such as Badami, Kesar, and Totapuri are classified using the ensemble method, Random forest. In the third phase, maturity detection is performed using another ensemble method, AdaBoost for the specific type of healthy mangoes to detect ripe, unripe, and partially ripe mangoes. Finally, in the fourth phase, size based grading is performed on the specific type and maturity to determine large, medium, and small mangoes using K-nearest neighbor. Thus the different grades of mangoes based on quality, type, maturity, and size are obtained which have different market price and demand. From experiments, the system shows 94.52% average accuracy.</p><p>Keywords: Quality inspection, classification, maturity detection, size based grading, feature vector, machine learning methods</p><p>Cite this Article Farhana Tazmim Pinki, S.M. Mohidul Islam. Quality Inspection, Maturity Detection, and Size based Grading of Various Types of Mangoes using Machine Learning Methods. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(1): 18–31p.</p>Farhana Tazmim PinkiS.M. Mohidul Islam2020-05-202020-05-208Machine Learning Approach to Determine Corrosion Potential of Friction Stir Welded Joints
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2378
<p>The main objective of the research study is to create an Artificial Neural Network (ANN) architecture to predict corrosion resistance from the Friction Stir Welding experimental dataset given to us. This is also to find the mean squared error and mean absolute percentage error of the given model which will help to analyze the losses and efficiency of the model. In the Artificial Neural Architecture, Tool Rotational Speed (rpm), Axial force (kN) and Welding Speed (mm/min) are the inputs while Corrosion Potential is the output. It is observed that different line plots for loss and mean square error the train plot loss decreases as epoch is increased while for test, the relation between loss and epoch remains constant at 0.4109.</p><p>Keywords: Neural Networks, Friction Stir Welding, Corrosion Potential, Machine Learning</p><p>Cite this Article Akshansh Mishra, Adarsh Tiwari, Vaibhav, Nitin Kumar Dubey. Machine Learning Approach to Determine Corrosion Potential of Friction Stir Welded Joints. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(1): 5–17p.</p>Akshansh MishraAdarsh TiwariVaibhav .Nitin Kumar Dubey2020-05-202020-05-208Detection of Cracks and its Length
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2346
<p>Numerous artworks, particularly old ones, experience the ill effects of breaks in the substrate, the paint, or the varnish. These examples are more often than not called splits and be brought about by maturing, drying and mechanical factors. The presence of splits on painting disintegrates the depictions quality. One can utilize advanced picture handling strategies to distinguish and dispense with the breaks on the digitized works of art. The primary target of this investigation is to introduce the advanced picture handling method that can be connected to the virtual reclamation of aesthetic artworks which serves numerous purposes. The strategies actualized in this study depend on concentrating on the advanced picture handling method utilized for breaks distinguishing proof and evacuation. Tangle lab is utilized to manufacture the code required to process and dissect the information. Cracks on the concrete surface are one of the most punctual signs of debasement of the structure which is basic for the upkeep too; the constant presentation will prompt the extreme harm to nature. Manual examination is the acclaimed strategy for the break review. In the manual examination, the draw of the split is arranged physically, and the states of the abnormalities are noted. Since the manual methodology totally relies upon the authority's information and experience, it needs objectivity in the quantitative investigation. Thus, programmed picture based break discovery is proposed as a substitution. The writing presents distinctive strategies to naturally distinguish the break and its profundity, utilizing picture preparing procedures. In this examination, a point by point study is led to recognize the exploration challenges and the accomplishments till in this field. In light of the survey, investigation is given dependent on the picture handling procedures, goals, precision level, mistake level, and the picture informational collections. At long last, we present the different research issues which can be helpful for the scientists to achieve further research on the break location. We can detect cracks in old paintings and arts and the quality of picture or image has to be good to be able to detect or analyze the crack in the painting. Ideas from various research papers are taken and combined to detect the cracks in the painting and know its length. The main strategy used in this work for crack detection is contrast stretching, image segmentation and morphological operations.</p><p>Keywords: Read-in image, crack detection, segmentation feature extraction, contrast stretching, pixel length, morphological operations</p><p>Cite this Article Purbasha Das. Detection of Cracks and its Length. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(1): 52–61p.</p>Purbasha Das2020-05-202020-05-208Detailed Analysis of Various Data Compression Techniques
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<p>Data compression refers to reducing the volume of space needed to keep data or reducing the amount of time required to transmit the data. The size of data is diminished by removing the unnecessary information. In this study many different data compression techniques have been studied, such as Shannon Fano, Shannon Fano Elias, Huffman binary and ternary coding. It is found that Huffman coding is the most suitable among these techniques as it provides smallest codeword length.</p><p>Keywords: Data compression, Shannon Fano, Shannon Fano Elias, Huffman binary coding, Huffman ternary coding</p><p>Cite this Article Shashank Gautam. Detailed Analysis of Various Data Compression Techniques. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(1): 1–4p.</p>Shashank Gautam2020-05-202020-05-208Local Binary Pattern-based Noise Robust Feature for Texture Classification
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2206
<p>Abstract: The presence of noise degrades the local binary pattern-based classification efficiency. In the present work, a modified local binary pattern ( —modified noise robust local binary pattern) based classification is proposed. In this, a local binary pattern-based feature is modified, which also captures macrostructure information, whereas the existing features capture microstructure texture information only. The new feature is tested on Outex_TC_00010, Outex_TC_00012 and Brodatz datasets for rotation invariant and noise robust texture classification. The texture images are degraded with multiplicative noise, to evaluate noise robustness of the feature. Nearest neighbour classifier is used for classification which minimises chi-square distance. The proposed feature gives promising results as it is rotation invariant, robust to noise and gives high classification accuracy, especially at high levels of noise.</p><p>Keywords: Texture classification, local binary pattern, feature extraction, histogram</p><p>Cite this Article: Simarjot Kaur Randhawa, Ramesh Kumar Sunkaria. Local Binary Pattern-based Noise Robust Feature for Texture Classification. Journal of Image Processing & Pattern Recognition Progress. 2019; 6(3): 31–47p.</p>Simarjot Kaur RandhawaRamesh Kumar Sunkaria2019-12-302019-12-308Language Independent Emotion Quantification using Non-linear Modelling of Speech
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2300
<p>Abstract: At present, emotion extraction from speech is a very important issue due to its diverse applications. Hence, it becomes absolutely necessary to obtain models that take into consideration the speaking styles of a person, vocal tract information, timbral qualities and other congenital information regarding his voice. Our speech production system is a nonlinear system like most other real-world systems. Hence, the need arises for modeling our speech information using nonlinear techniques. In this work, we have modeled our articulation system using nonlinear multifractal analysis. The multifractal spectral width and scaling exponents reveal essentially the complexity associated with the speech signals taken. The multifractal spectrums are well distinguishable the in low-fluctuation region in case of different emotions. The source characteristics have been quantified with the help of different nonlinear models like multifractal detrended fluctuation analysis (MFDFA), wavelet transform modulus maxima (WTMM). The results obtained from this study give a very good result in emotion clustering.</p><p>Keywords: Emotional speech, categorization, multifractal detrended fluctuation analysis (MFDFA), wavelet transform modulus maxima (WTMM)</p><p>Cite this Article: Uddalok Sarkar, Sayan Nag, Chirayata Bhattacharyaa, Shankha Sanyal, Archi Banerjee, Ranjan Sengupta, Dipak Ghosh. Language Independent Emotion Quantification using Nonlinear Modeling of Speech. Journal of Image Processing & Pattern Recognition Progress. 2019; 6(3): 24–30p.</p>Uddalok SarkarSayan NagChirayata BhattacharyaaShankha SanyalArchi BanerjeeRanjan SenguptaDipak Ghosh2019-12-302019-12-308Automatic Detection of Diabetic Retinopathy from Fundus Images- A Review
https://computers.stmjournals.com/index.php?journal=JoIPPRP&page=article&op=view&path%5B%5D=2242
<p>Abstract: Diabetes mellitus may cause modifications in the retinal microvasculature prompting diabetic retinopathy (DR). Unchecked, propelled/advanced DR may prompt visual impairment. It very well may be dull and tedious to unravel unpretentious morphological changes in optic disc, microaneurysms, haemorrhages, veins, macula, and exudates through manual review of fundus pictures/images. A computer aided systematic diagnosis can considerably decrease the burden on ophthalmologists and can increase the inter- and intra-observer variability. This review discusses about the features of DR, accessible and available methods of automated analysis and methods in retinal image screening for DR.</p><p>Keywords: Retinopathy, exudates, retina, pre-processing, classifiers</p><p>Cite this Article: Tawseef Ahmad Dar, Amjad Husain. Automatic Detection of Diabetic Retinopathy from Fundus Images: A Review. Journal of Image Processing & Pattern Recognition Progress. 2019; 6(3): 12–23p.</p>Tawseef Ahmad DarAmjad Husain2019-12-302019-12-308