i-manager’s Journal on Image Processing最新文献

{"title":"UNDERWATER IMAGE ENHANCEMENT USING \u0000VERY DEEP SUPER RESOLUTION TECHNIQUE","authors":"M. Reddy, T. Ramashri","doi":"10.26634/jip.8.2.18323","DOIUrl":"https://doi.org/10.26634/jip.8.2.18323","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124619308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fruit recognition using image processing","authors":"Sahu Pratibha, Dewangan Abhishek, Mandal Snehlata","doi":"10.26634/jip.9.3.19047","DOIUrl":"https://doi.org/10.26634/jip.9.3.19047","url":null,"abstract":"Manually classifying and evaluating anything is difficult. It is difficult to manually count ripe fruits and evaluate their quality. Increasing labor costs, a shortage of skilled workers, and declining storage costs are just some of the major challenges associated with fruit production, marketing, and storage, among others. An effective method for localizing all clearly visible objects or portion of an object from an image has been proposed in this study, requiring less memory and processing resources. The main obstacles for object detection, such as object overlap, background noise, low resolution, etc, that prevents us from obtaining better results has been overcome by processing every input image. It also built an enhanced classification or recognition algorithm based on convolutional neural networks, which has shown to perform better than baseline studies.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128238827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DOCUMENT IMAGE ANALYSIS AND ENHANCEMENT - A BRIEF\u0000REVIEW ON DIGITAL PRESERVATION","authors":"Prakash Saxena Lalit","doi":"10.26634/jip.8.1.17380","DOIUrl":"https://doi.org/10.26634/jip.8.1.17380","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122544582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SKIN TEXTURE RECOGNITION THROUGH IMAGE PROCESSING","authors":"Sharma Sukhdeep, Dubey Aayushi","doi":"10.26634/jip.6.1.15560","DOIUrl":"https://doi.org/10.26634/jip.6.1.15560","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130014801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lungs disease detection using image processing through python","authors":"Sahu Tikendra, S. Aakanksha","doi":"10.26634/jip.9.1.18550","DOIUrl":"https://doi.org/10.26634/jip.9.1.18550","url":null,"abstract":"The novel coronavirus disease (COVID-19), with a start line in China, has spread hastily amongst human beings dwelling in other international locations, and is coming near approximately 34,986,502 instances worldwide in line with the facts of Edith Cowan University (ecu) Centre for disorder prevention and control. There are a restrained number of COVID-19 test kits to be had in hospitals due to the growing cases day by day. Consequently, it is important to implement an automated detection machine as a brief opportunity diagnosis choice to prevent COVID-19 spreading among human beings. Fusion was considered as a concatenation between the two-person vectors on this context. Speckle-affected and coffee-fine X-ray images along with top first-class pictures have been utilized in our test for carrying out exams. If training and trying out are done with best selected right fine X-ray photos in a super situation, the output accuracy can be observed higher. However, this doesn't constitute a real-existence situation, wherein the photo database would be a mixture of each appropriate- and poor-first-rate pictures. Therefore, this technique of the use of different excellent snap shots could test how nicely the machine can react to such real-lifestyles situations. A modified anisotropic diffusion filtering technique become hired to take away multiplicative speckle noise from the test photographs. The software of these techniques ought to successfully conquer the restrictions in enter photograph quality. Subsequent, the function extraction changed into finished on the test photographs. Ultimately, the Convolutional Neural Network (CNN) classifier accomplished a type of X-ray photographs to pick out whether or not it changed into COVID-19 or until now. Pneumonia, an interstitial lung sickness, is the main reason of loss of life in children under the age of five. It accounted for approximately 16% of the deaths of kids below the age of 5, killing around 880,000 kids in 2016 according to a look at conducted with the aid of United Nations International Children's Emergency Fund (UNICEF). Affected children were mostly much less than two years old. Well timed detection of pneumonia in youngsters can assist to the technique of restoration. This paper gives convolutional neural community fashions to accurately hit upon pneumonic lungs from chest X-rays, which can be utilized inside the actual global by using medical practitioners to treat pneumonia. Experimentation was conducted on Chest X-Ray images dataset to be had on Kaggle.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127189867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facial emotion recognition using hybrid features-novel leaky rectified triangle linear unit activation function based deep convolutional neural network","authors":"Suputri Devi D. Anjani, E. Suneetha","doi":"10.26634/jip.9.2.18968","DOIUrl":"https://doi.org/10.26634/jip.9.2.18968","url":null,"abstract":"Facial Expression Recognition (FER) is an important topic that is used in many areas. FER categorizes facial expressions according to human emotions. Most networks are designed for facial emotion recognition but still have some problems, such as performance degradation and the lowest classification accuracy. To achieve greater classification accuracy, this paper proposes a new Leaky Rectified Triangle Linear Unit (LRTLU) activation function based on the Deep Convolutional Neural Network (DCNN). The input images are pre-processed using the new Adaptive Bilateral Filter Contourlet Transform (ABFCT) filtering algorithm. The face is then detected in the filtered image using the Chehra face detector. From the detected face image, facial landmarks are extracted using a cascading regression tree, and important features are extracted based on the detected landmarks. The extracted feature set is then passed as input to the Leaky Rectified Triangle Linear Unit Activation Function Based Deep Convolutional Neural Network (LRTLU-DCNN), which classifies the input image expressions into six emotions, such as happiness, sadness, neutrality, anger, disgust, and surprise. Experimentation of the proposed method is carried out using the Extended Cohn-Kanade (CK+) and Japanese Female Facial Expression (JAFFE) datasets. The proposed work provides a classification accuracy of 99.67347% for the CK+ dataset along with 99.65986% for the JAFFE dataset.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127966065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Convolutional Neural Network with a Stochastic Gradient Descent Optimizer (PDCNN-SGD) model for telugu character recognition","authors":"Siva Phaniram Josyula, Reddy M. Babu","doi":"10.26634/jip.10.1.19250","DOIUrl":"https://doi.org/10.26634/jip.10.1.19250","url":null,"abstract":"Telugu Character Recognition (TCR) has received significant attention because of the drastic increase in technological advancements such as multimedia, smartphones and iPods, and paper documents. Offline character recognition is the process of identifying Telugu characters from the scanned image or document whereas online character recognition enables to recognition of characters by the machine while the user writes. Several researchers have attempted to design online TCR models by the use of distinct classification models and feature extraction approaches. It is still necessary to construct automated and intelligent online TCR models, even if many studies have focused on offline TCR models. The Telugu character dataset construction and validation using an Inception and ResNet-based model are presented. The collection of 645 letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34*16 Guninthamulu and 10 Ankelu. The proposed technique aims to efficiently recognize and identify distinctive Telugu characters online. This model's main preprocessing steps to achieve its goals include normalization, smoothing, and interpolation. Improved recognition performance can be attained by using Stochastic Gradient Descent (SGD) to optimize the model's hyperparameters.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133574913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A REVIEW ON DEEP LEARNING OF NEURAL NETWORK BASED\u0000IMAGE COMPRESSION TECHNIQUES","authors":"Kanti Das Shubhajit, Chakraborty Abir","doi":"10.26634/jip.6.3.16342","DOIUrl":"https://doi.org/10.26634/jip.6.3.16342","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"887 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130752064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A BLIND APPROACH OF QR CODE BASED COLOR\u0000 IMAGE WATERMARKING USING DWT","authors":"Rao K. Lokeswara, B. Jagadeesh, A. Lekhamrutha","doi":"10.26634/jip.6.2.16521","DOIUrl":"https://doi.org/10.26634/jip.6.2.16521","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116570129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PERFORMANCE ANALYSIS OF COPY-MOVE\u0000 FORGERY DETECTION TECHNIQUES","authors":"Suresh Gulivindala, S. R. Chanamallu","doi":"10.26634/JIP.6.1.15925","DOIUrl":"https://doi.org/10.26634/JIP.6.1.15925","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116637604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}