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Projectable Classifiers for Multi-View Object Class Recognition
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2011 (English)In: 3rd International IEEE Workshop on 3D Representation and Recognition, 2011Conference paper, Published paper (Refereed)
Abstract [en]

We propose a multi-view object class modeling framework based on a simplified camera model and surfels (defined by a location and normal direction in a normalized 3D coordinate system) that mediate coarse correspondences between different views. Weak classifiers are learnt relative to the reference frames provided by the surfels. We describe a weak classifier that uses contour information when its corresponding surfel projects to a contour element in the image and color information when the face of the surfel is visible in the image. We emphasize that these weak classifiers can possibly take many different forms and use many different image features. Weak classifiers are combined using AdaBoost. We evaluate the method on a public dataset [8], showing promising results on categorization, recognition/detection, pose estimation and image synthesis.

Place, publisher, year, edition, pages
2011.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-49913DOI: 10.1109/ICCVW.2011.6130295ISI: 000300056700080Scopus ID: 2-s2.0-84856685061ISBN: 978-1-4673-0063-6 (print)OAI: oai:DiVA.org:kth-49913DiVA: diva2:460615
Conference
3rd International IEEE Workshop on 3D Representation and Recognition (3dRR-11). Barcellona, Spain. November 07, 2011 - November 07, 2011
Note

QC 20111205

Available from: 2011-11-30 Created: 2011-11-30 Last updated: 2012-08-29Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf