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A Self-Occlusion Detection Approach Based on Depth Image Using SVM : Approccio per l'dentificazione di auto occlusioni basato sulla profondità dell'immagine utilizzando SVM, in: International Journal of Advanced Robotic Systems

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Autor Zhang, Shi-hui; Liu, Jian-xin
Erschienen  InTech Open Access Publisher, 2012
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Kurzbeschreibung This paper describes a novel self‐occlusion
detection approach for depth image using SVM. This
work is distinguished by three contributions. The first
contribution is the introduction of a new self‐occlusion
detection idea, which takes the self‐occlusion as a
classification problem for the first time, thus the accuracy
of the detection result is improved. The second
contribution is two new self‐occlusion‐related features,
named maximal depth difference and included angle. The
third contribution is a specific self‐occlusion detection
algorithm. Experimental results not only show that the
proposed approach is feasible and effective, but also
show that our works produce better results than those
previously published.
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Zeitschriftenartikel
ab 2000
Übergeordnete Werke
 
no fulltext found International Journal of Advanced Robotic Systems
Autor: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Erschienen: 2004
Verknüpfte Datensätze
Dokumente: International Journal of Advanced Robotic Systems
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DMG-Lib FaviconDMG-Lib https://www.dmg-lib.org/dmglib/handler?docum=33015009
Europeana FaviconEuropeana  http://www.europeana.eu/portal/record/2020801/dmglib_handler_docum_33015009.html
PDF FaviconPDF  A Self-Occlusion Detection Approach Based on Depth Image Using SVM
Datenbereitsteller
UCAUniv. Cassino  http://webuser.unicas.it/weblarm/larmindex.htm
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Publikationsdatum 2012
Lizenzinformation Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License

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