Extracting essential local object characteristics for 3D object categorization
2013 (English)In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2013, 2240-2247 p.Conference paper (Refereed)
Most object classes share a considerable amount of local appearance and often only a small number of features are discriminative. The traditional approach to represent an object is based on a summarization of the local characteristics by counting the number of feature occurrences. In this paper we propose the use of a recently developed technique for summarizations that, rather than looking into the quantity of features, encodes their quality to learn a description of an object. Our approach is based on extracting and aggregating only the essential characteristics of an object class for a task. We show how the proposed method significantly improves on previous work in 3D object categorization. We discuss the benefits of the method in other scenarios such as robot grasping. We provide extensive quantitative and qualitative experiments comparing our approach to the state of the art to justify the described approach.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 2240-2247 p.
, IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858
computer vision, robotics, 3D vision, object recognition
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-136370DOI: 10.1109/IROS.2013.6696670ISI: 000331367402060ScopusID: 2-s2.0-84893753374ISBN: 978-146736358-7OAI: oai:DiVA.org:kth-136370DiVA: diva2:675929
2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013; Tokyo; Japan; 3 November 2013 through 8 November 2013
FunderEU, FP7, Seventh Framework Programme, IST-FP7-270436EU, FP7, Seventh Framework Programme, FP7- ICT-288533Swedish Foundation for Strategic Research Swedish Research Council
QC 201312162013-12-042013-12-042014-04-10Bibliographically approved