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Titlebook: Image Analysis and Processing – ICIAP 2022; 21st International C Stan Sclaroff,Cosimo Distante,Federico Tombari Conference proceedings 2022

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发表于 2025-3-21 19:18:16 | 显示全部楼层 |阅读模式
书目名称Image Analysis and Processing – ICIAP 2022
副标题21st International C
编辑Stan Sclaroff,Cosimo Distante,Federico Tombari
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Image Analysis and Processing – ICIAP 2022; 21st International C Stan Sclaroff,Cosimo Distante,Federico Tombari Conference proceedings 2022
描述The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy,.The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc. .
出版日期Conference proceedings 2022
关键词computer security; computer vision; data security; Human-Computer Interaction (HCI); image analysis; imag
版次1
doihttps://doi.org/10.1007/978-3-031-06430-2
isbn_softcover978-3-031-06429-6
isbn_ebook978-3-031-06430-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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0302-9743 iew geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc. .978-3-031-06429-6978-3-031-06430-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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A Strong Geometric Baseline for Cross-View Matching of Multi-person 3D Pose Estimation from Multi-vion, a novel non-deep learning and parameter-free algorithm is developed to this end, which is only based on geometrical cues (i.e. reprojection errors). This is shown to achieve perfect precision and recall figures on a challenging 102-person benchmark.
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U Can’t (re)Touch This – A Deep Learning Approach for Detecting Image Retouchingxperiment tries to distinguish all ten classes at once..Overall, the first experiment achieves an accuracy of over 99% for most classes, and the second experiments a total accuracy of roughly 88%. Finally, we analyze, why some of the classes have lower accuracies.
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Deepfake Style Transfer Mixture: A First Forensic Ballistics Study on Synthetic Imagesnerative architecture for style transfer. Moreover, in order to address and study accurately forensic ballistics on deepfake images, some mathematical properties of style-transfer operations were investigated.
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