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Matching objects across the textured-smooth continuum

Arandjelovic, Ognjen 2012, Matching objects across the textured-smooth continuum, in ACRA 2012 : Proceedings of Australasian Conference on Robotics and Automation, Australian Robotics and Automation Association, Sydney, N.S.W., pp. 1-8.

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Title Matching objects across the textured-smooth continuum
Author(s) Arandjelovic, Ognjen
Conference name Australasian Conference on Robotics and Automation (2012: Wellington, New Zealand)
Conference location Wellington, New Zealand
Conference dates 3-5 Dec. 2012
Title of proceedings ACRA 2012 : Proceedings of Australasian Conference on Robotics and Automation
Editor(s) [Unknown]
Publication date 2012
Conference series Australasian Conference on Robotics and Automation
Start page 1
End page 8
Total pages 8
Publisher Australian Robotics and Automation Association
Place of publication Sydney, N.S.W.
Summary The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem of matching smooth objects using boundary features. However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein. Due to the complementarity of the two modalities, we fuse the corresponding matching scores and learn their relative weighting in a data specific manner by optimizing discriminative performance on synthetically distorted data. For the textural description of an object we adopt a representation in the form of a histogram of SIFT based visual words. Similarly the apparent shape of an object is represented by a histogram of discretized features capturing local shape. On a large public database of a diverse set of objects, the proposed method is shown to outperform significantly both purely textural and purely shape based approaches for matching across viewpoint variation.
ISBN 9780980740431
ISSN 1448-2053
Language eng
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, Australian Robotics and Automation Association
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30052649

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.