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

{"title":"DEVELOPMENT OF A MORPHOLOGICAL IMAGE\u0000 ANALYSIS BASED QUALITY EVALUATION SYSTEM\u0000 FOR FRUITS AND VEGETABLES","authors":"R. Leela, B. Sridhar, Nadipena Harika, L. Rahul","doi":"10.26634/jip.6.4.16812","DOIUrl":"https://doi.org/10.26634/jip.6.4.16812","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"26 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":"133035289","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 Parkinson's disease diagnosis using machine learning techniques","authors":"Chnachal, M. Megha, Kumar Mishra Vishnu","doi":"10.26634/jip.10.1.19381","DOIUrl":"https://doi.org/10.26634/jip.10.1.19381","url":null,"abstract":"The decreased production of dopamine in the forebrain is believed to be the underlying cause of Parkinson's disease, a neurodegenerative disorder that affects the nervous system. Parkinson's disease is a chronic and progressive illness that may develop new symptoms over time (Nilashi et al., 2016). This occurs as neurons in the substantia nigra of the brain gradually die. People with Parkinson's disease may find it difficult to perform everyday tasks in the workplace. Although clinical evaluations consider a significant amount of data that includes various aspects, it is not always easy to determine whether a person has PD based on this data alone. Feature selection methods can help address this issue. Various techniques are being researched, developed, and evaluated for diagnosing Parkinson's disease, based on the relevant information. This study provides an overview of the use of machine learning algorithms to predict Parkinson's disease, as well as the various new technologies that have been developed and the accuracy that has been achieved.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"1 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":"129012007","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":"STANDARD PARTICLE SWARM OPTIMIZATION ALGORITHM FOR\u0000IMAGE ENHANCEMENT","authors":"Mani Kumar Jogi, Y. Rao","doi":"10.26634/jip.8.4.18441","DOIUrl":"https://doi.org/10.26634/jip.8.4.18441","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"76 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":"133882470","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":"IMPLEMENTATION OF IMAGE ANALYSIS SYSTEM BY USING CONVOLUTION NEURAL NETWORKS","authors":"B. Sridhar, Ch. Sowjanya, Vamsi Krishna Chinta, Rao D. Govind","doi":"10.26634/jip.6.4.16813","DOIUrl":"https://doi.org/10.26634/jip.6.4.16813","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"57 3-4 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":"132627831","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":"BLOCK MOTION ESTIMATION BASED FIRE DETECTION","authors":"S. Sruthi, B. Anuradha","doi":"10.26634/jip.8.2.18104","DOIUrl":"https://doi.org/10.26634/jip.8.2.18104","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"54 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":"133438284","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":"Vehicular detection technique using image processing","authors":"R. Sheryl, Punitha Malar Dhas Julia, Kirubakaran S. Stewart","doi":"10.26634/jip.9.3.19142","DOIUrl":"https://doi.org/10.26634/jip.9.3.19142","url":null,"abstract":"The increasing traffic volume creates a greatest challenge in today's traffic research. It is important to know the road traffic density for effective traffic management and Intelligent Transportation System (ITS). Traditional method of detecting vehicles from video is image subtraction which is not effective as it is susceptible to changing levels of brightness. And hence the proposed algorithm detects vehicles from an image in a more precise manner. The proposed work uses image processing that involves the techniques such as image acquisition, image enhancement, and Image segmentation to retrieve image from the source and enhance the contrast and brightness of the image for successful surveillance on transportation system.","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":"114305148","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":"IMPLEMENTATION OF APPROXIMATE ADDERS AND MULTIPLIERS\u0000FOR ERROR TOLERANT IMAGE PROCESSING","authors":"P. Mani, S. Priyadharshini, N. Priyanga, V. Reshma","doi":"10.26634/jip.8.3.18218","DOIUrl":"https://doi.org/10.26634/jip.8.3.18218","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"167 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":"121462598","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":"Enhanced approach for brain tumor detection","authors":"Kumar Mar, Patil Vinuta, Rachamalla Sushitha, Gajulavarthi Hepseeba, Bhavana Martha","doi":"10.26634/jip.10.2.19818","DOIUrl":"https://doi.org/10.26634/jip.10.2.19818","url":null,"abstract":"Automated defect detection in medical imaging has become an emerging field in several medical diagnostic applications. Automated detection of tumors in MRI is crucial as it provides information about abnormal tissues that are necessary for treatment. The conventional method for defect detection in magnetic resonance brain images is human inspection. This method is impractical due to the large amount of data. Hence, trusted and automatic classification schemes are essential to preventing the human death rate. So, automated tumor detection methods are being developed to save radiologist time and obtain tested accuracy. MRI brain tumor detection is a complicated task due to the complexity and variability of tumors. In this work, machine learning algorithms are proposed to overcome the drawbacks of traditional classifiers when tumors are detected in brain MRIs using machine learning algorithms. The outcome of the model is to predict whether a tumor is present or not in the image.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"22 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":"125414249","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":"Geological map feature extraction using object detection techniques - a comparative analysis","authors":"P. A. N. Dilhan, R. Siyambalapitiya","doi":"10.26634/jip.9.2.18916","DOIUrl":"https://doi.org/10.26634/jip.9.2.18916","url":null,"abstract":"Conducting a geological field survey at the initial stage is an important step in geo-oriented projects and construction. Therefore, better and more accurate solutions are only possible with field analysis and proper modeling. The geological modeling process takes a long time, especially depending on the area of interest. It is inefficient to digitize 2D geological maps with traditional software that uses manual user interaction. This paper proposes a state-of-the-art feature detection methodology for detecting geological features on high-resolution maps. With the development of efficient deep learning algorithms and the improvement of hardware systems, the accuracy of detecting specific objects in digital images, such as human facial features, has reached more than 90%. Current object detection models based on convolutional neural networks cannot be directly applied to high-resolution geological maps due to the input image size limitations of conventional object detection solutions, mostly limited by hardware resources. This paper proposes a sliding window method for character detection of geological features. Detection models are trained using transfer learning with You Look Only Once-v3 (YOLO-v3), Single Shot Multi-Box Detector (SSD), Faster-Region-based Convolutional Neural Network (Faster-RCNN), and Single Shot Multi-Box Detector_RetinaNet (SSD_RetinaNet). All models provide competitive success rates with an average precision (AP) of 0.96 on YOLOv3, 0.88 AP on EfficientNet, 0.92 AP on Faster- RCNN, and 0.97 AP on SSD_RetinaNet. YOLOv3 outperformed the best detection over SSD according to F1 recall and score. Since the input size of detection models is limited, a sliding window algorithm is used to separate high-resolution map images. The final detected strike features are provided as a digital dataset that can be used for further manipulations. Thus, Convolutional Neural Network (CNN) based object detection along with a sliding window protocol can be applied to manual map digitization processes to provide instantaneous digitized data with higher accuracy. This automated process can be used to detect small features and digitize other high-resolution drawings.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"1 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":"125790602","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":"COMPARISON ON AUTOMATED BRAIN TUMOR DETECTION AND\u0000SEGMENTATION APPROACHES FOR MRI BRAIN IMAGES","authors":"P. Sirisha, D. Haritha","doi":"10.26634/jip.6.3.16322","DOIUrl":"https://doi.org/10.26634/jip.6.3.16322","url":null,"abstract":"","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"17 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":"129082758","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}