Pub Date : 2023-04-20DOI: 10.1080/13682199.2023.2204036
A. Selvakumar, A. Balasundaram
{"title":"Automated Mango Leaf Infection Classification using Weighted and Deep Features with Optimized Recurrent Neural Network Concept","authors":"A. Selvakumar, A. Balasundaram","doi":"10.1080/13682199.2023.2204036","DOIUrl":"https://doi.org/10.1080/13682199.2023.2204036","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77821681","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}
Pub Date : 2023-04-19DOI: 10.1080/13682199.2023.2202088
Ahmed S. Mashaly, T. Mahmoud
{"title":"Phase image-guided adaptive rotation-invariant feature point detector","authors":"Ahmed S. Mashaly, T. Mahmoud","doi":"10.1080/13682199.2023.2202088","DOIUrl":"https://doi.org/10.1080/13682199.2023.2202088","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84346869","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}
Pub Date : 2023-04-18DOI: 10.1080/13682199.2023.2199504
R. Rashmi, U. Snekhalatha, Anela L. Salvador, A. Raj
{"title":"Facial emotion detection using thermal and visual images based on deep learning techniques","authors":"R. Rashmi, U. Snekhalatha, Anela L. Salvador, A. Raj","doi":"10.1080/13682199.2023.2199504","DOIUrl":"https://doi.org/10.1080/13682199.2023.2199504","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74634998","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}
Pub Date : 2023-04-17DOI: 10.1080/13682199.2023.2198350
V. Karthikeyan, E. Raja, D. Pradeep
{"title":"Energy based denoising convolutional neural network for image enhancement","authors":"V. Karthikeyan, E. Raja, D. Pradeep","doi":"10.1080/13682199.2023.2198350","DOIUrl":"https://doi.org/10.1080/13682199.2023.2198350","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90784512","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}
Pub Date : 2023-04-11DOI: 10.1080/13682199.2023.2196494
Shiju Samuel, R. S. Ochawar, M. Rukmini
{"title":"Hybrid deep autoencoder network based adaptive cross guided bilateral filter for motion artifacts correction and denoising from MRI","authors":"Shiju Samuel, R. S. Ochawar, M. Rukmini","doi":"10.1080/13682199.2023.2196494","DOIUrl":"https://doi.org/10.1080/13682199.2023.2196494","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77909418","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}
Pub Date : 2023-04-09DOI: 10.1080/13682199.2023.2195121
J. Evangelin, Deva Sheela, P. Arockia, J. Rani, M. A. Paul
ABSTRACT Object detection has become a very prominent subject for research in recent times. This study's main goal is to suggest a technique for video saliency object detection. It seems to sense that using the depth information in photos to detect salient things. Since depth offers abundant information about scene structure, object forms, and other 3D cues. This information is very compatible to distinguish between objects in the foreground and background. As a result of the high object density, small object size, and cluttered background, aerial photos and movies provide results with low precision. In this paper, the proposed SPTM (Super Pixel Transmission Map)-YOLO model, the input RGB image has applied Dark Channel Prior (DCP) method for estimating the transmission map. From the transmission map only, the background probability is estimated with the help of SLIC (simple linear iterative clustering algorithm) superpixel segmentation. That foreground extracted image is further learned with YOLO architecture to detect the objects effectively. For object detection in aerial images, this proposed SPTM-YOLO approach outperforms classic YOLO by up to 6% accuracy. Accurate detection of things that are small in size, partially occluded, and out of view is possible.
{"title":"Super pixels transmission map-based object detection using deep neural network in UAV video","authors":"J. Evangelin, Deva Sheela, P. Arockia, J. Rani, M. A. Paul","doi":"10.1080/13682199.2023.2195121","DOIUrl":"https://doi.org/10.1080/13682199.2023.2195121","url":null,"abstract":"ABSTRACT Object detection has become a very prominent subject for research in recent times. This study's main goal is to suggest a technique for video saliency object detection. It seems to sense that using the depth information in photos to detect salient things. Since depth offers abundant information about scene structure, object forms, and other 3D cues. This information is very compatible to distinguish between objects in the foreground and background. As a result of the high object density, small object size, and cluttered background, aerial photos and movies provide results with low precision. In this paper, the proposed SPTM (Super Pixel Transmission Map)-YOLO model, the input RGB image has applied Dark Channel Prior (DCP) method for estimating the transmission map. From the transmission map only, the background probability is estimated with the help of SLIC (simple linear iterative clustering algorithm) superpixel segmentation. That foreground extracted image is further learned with YOLO architecture to detect the objects effectively. For object detection in aerial images, this proposed SPTM-YOLO approach outperforms classic YOLO by up to 6% accuracy. Accurate detection of things that are small in size, partially occluded, and out of view is possible.","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88105257","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}
ABSTRACT Reversible data hiding (RDH) is a special class of steganography that is capable of recovering the original cover image upon the extraction of the secret data. The main goal of this paper is to develop different adaptive predictors based on superpixel irregular block sorting. Firstly, a superpixel irregular block and sorting strategy is proposed which is applied to histogram shifting for the first time. Then, a multi-directional edge classification method is proposed, which divides pixels into strong edge pixels, normal edge pixels, and weak edge pixels. Moreover, strong edge pixels and normal edge pixels are further divided into four directions. According to edge classification, the most appropriate adaptive multi-predictor is proposed. Finally, an optimization-based data hiding strategy is proposed. The proposed scheme focuses on constructing a sharp enough histogram. The investigational results demonstrate that the proposed scheme achieves large capacity, high image quality, and low complexity.
{"title":"Adaptive multi-predictor based reversible data hiding with superpixel irregular block sorting and optimization","authors":"Hui Shi, Baoyue Hu, Yanli Li, Jianing Geng, Yonggong Ren","doi":"10.1080/13682199.2023.2195090","DOIUrl":"https://doi.org/10.1080/13682199.2023.2195090","url":null,"abstract":"ABSTRACT\u0000 Reversible data hiding (RDH) is a special class of steganography that is capable of recovering the original cover image upon the extraction of the secret data. The main goal of this paper is to develop different adaptive predictors based on superpixel irregular block sorting. Firstly, a superpixel irregular block and sorting strategy is proposed which is applied to histogram shifting for the first time. Then, a multi-directional edge classification method is proposed, which divides pixels into strong edge pixels, normal edge pixels, and weak edge pixels. Moreover, strong edge pixels and normal edge pixels are further divided into four directions. According to edge classification, the most appropriate adaptive multi-predictor is proposed. Finally, an optimization-based data hiding strategy is proposed. The proposed scheme focuses on constructing a sharp enough histogram. The investigational results demonstrate that the proposed scheme achieves large capacity, high image quality, and low complexity.","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83996594","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}
Pub Date : 2023-04-02DOI: 10.1080/13682199.2023.2180140
Sangeetha Yempally, S. K. Singh, Velliangiri Sarveshwaran
ABSTRACT Currently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous security-sensitive data; therefore, device security is considered an important concept. The main aim of this work is to formulate a secure key generation process in the data-sharing approach by exploiting the Rider Horse Herd Optimization Algorithm (RHHO). Here, eight phases, like the initialization phase, registration phase, key generation phase, login phase, data protection phase, authentication phase, verification phase, and data decryption phase are exploited for secure and efficient authentication and multimedia data sharing. The proposed RHHO model is the integration of the Rider Optimization Algorithm (ROA) and Horse herd Optimization Algorithm (HOA). The proposed RHHO model achieved enhanced performance with a computation cost of 0.235, an accuracy of 0.935and memory usage of 2.425 MB.
{"title":"A secure and efficient authentication and multimedia data sharing approach in IoT-healthcare","authors":"Sangeetha Yempally, S. K. Singh, Velliangiri Sarveshwaran","doi":"10.1080/13682199.2023.2180140","DOIUrl":"https://doi.org/10.1080/13682199.2023.2180140","url":null,"abstract":"ABSTRACT Currently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous security-sensitive data; therefore, device security is considered an important concept. The main aim of this work is to formulate a secure key generation process in the data-sharing approach by exploiting the Rider Horse Herd Optimization Algorithm (RHHO). Here, eight phases, like the initialization phase, registration phase, key generation phase, login phase, data protection phase, authentication phase, verification phase, and data decryption phase are exploited for secure and efficient authentication and multimedia data sharing. The proposed RHHO model is the integration of the Rider Optimization Algorithm (ROA) and Horse herd Optimization Algorithm (HOA). The proposed RHHO model achieved enhanced performance with a computation cost of 0.235, an accuracy of 0.935and memory usage of 2.425 MB.","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87696733","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}
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