Computer vision systems : 9th International Conference, ICVS 2013, St. Petersburg, Russia, July 16-18, 2013, Proceedings / edited by Mei Chen, Bastian Leibe, Bernd Neumann.Material type: TextSeries: Serienbezeichnung | Lecture notes in computer science ; 7963. | LNCS sublibrary. SL 1, Theoretical computer science and general issues.Publication details: Berlin : Springer, ©2013. Description: 1 online resource (xxii, 366 pages) : illustrations (black and white)Content type: text Media type: computer Carrier type: online resourceISBN: 9783642394027; 3642394027Other title: ICVS 2013Subject(s): Computer vision -- Congresses | Image processing -- Digital techniques -- Congresses | Pattern recognition systems -- Congresses | Artificial Intelligence | Image Processing, Computer-Assisted | Computer Graphics | Computer vision | Image processing -- Digital techniques | Pattern recognition systemsGenre/Form: Congress. | Electronic books. | Ebook. | Conference papers and proceedings. Additional physical formats: Print version:: Computer Vision Systems: 9th International Conference. Petersburg, Icvs 2013, St. Petersburg, Russia, July 16-18, 2013. Proceedings.DDC classification: 006.3/7 LOC classification: TA1634 | .I28 2013NLM classification: TA 1634Online resources: Click here to access online
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International conference proceedings.
Includes author index.
Print version record.
This book constitutes the refereed proceedings of the 9th International Conference on Computer Vision Systems, ICVS 2013, held in St. Petersburg, Russia, July 16-18, 2013. Proceedings. The 16 revised papers presented with 20 poster papers were carefully reviewed and selected from 94 submissions. The papers are organized in topical sections on image and video capture; visual attention and object detection; self-localization and pose estimation; motion and tracking; 3D reconstruction; features, learning and validation.
Image and video capture -- visual attention and object detection -- self-localization and pose estimation -- motion and tracking -- 3D reconstruction -- features, learning and validation.