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Titlebook: Computer Vision and Image Processing; 6th International Co Balasubramanian Raman,Subrahmanyam Murala,Puneet G Conference proceedings 2022 T

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发表于 2025-3-21 17:20:42 | 显示全部楼层 |阅读模式
书目名称Computer Vision and Image Processing
副标题6th International Co
编辑Balasubramanian Raman,Subrahmanyam Murala,Puneet G
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Computer Vision and Image Processing; 6th International Co Balasubramanian Raman,Subrahmanyam Murala,Puneet G Conference proceedings 2022 T
描述This two-volume set (CCIS 1567-1568) constitutes the refereed proceedings of the 6h International Conference on Computer Vision and Image Processing, CVIP 2021, held in Rupnagar, India, in December 2021. .The 70 full papers and 20 short papers were carefully reviewed and selected from the 260 submissions. The papers present recent research on such topics as biometrics, forensics, content protection, image enhancement/super-resolution/restoration, motion and tracking, image or video retrieval, image, image/video processing for autonomous vehicles, video scene understanding, human-computer interaction, document image analysis, face, iris, emotion, sign language and gesture recognition, 3D image/video processing, action and event detection/recognition, medical image and video analysis, vision-based human GAIT analysis, remote sensing, and more..
出版日期Conference proceedings 2022
关键词artificial intelligence; computer networks; computer systems; computer vision; databases; deep learning; i
版次1
doihttps://doi.org/10.1007/978-3-031-11349-9
isbn_softcover978-3-031-11348-2
isbn_ebook978-3-031-11349-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Internationalization and Localization,using Channel Attention Block (CAB) and Residual Block (ResB) respectively. An extensive quantitative and qualitative analysis of the proposed method is done on benchmark synthetic hazy video database namely DAVIS-16 and NYU depth. Experimental result shows that the proposed method outperforms the o
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Internationalization and Localization, does not require labels. It requires only a single pass through the video, with no separate training set. Given the lack of datasets of very long videos, we demonstrate our method on video from 10 d (254 h) of continuous wildlife monitoring data that we had collected with required permissions. We f
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From Eclipse RCP to the NetBeans Platform,ion carried out reveals that traditional machine learning performs well when trained and tested on a single dataset. However, there is a drastic change in the performance of machine learning models when tested on a different ship dataset. The results show that the deep learning models have better fe
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https://doi.org/10.1007/978-1-4302-2418-1dge, this is the first work with CDM based auto-encoder for FCID. The performance of the proposed network is tested on benchmark datasets and compared with the existing state-of-the-art methods for FCID in terms of half total error rate (HTER). The experimental results demonstrate that the proposed
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