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Object matching using boundary descriptors

Arandjelovic, Ognjen 2012, Object matching using boundary descriptors, in BMVC 2012 : Proceeding of the British Machine Vision Conference 2012, BMVA Press, [Surrey, England], pp. 1-11, doi: 10.5244/C.26.85.

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Title Object matching using boundary descriptors
Author(s) Arandjelovic, Ognjen
Conference name British Machine Vision. Conference (2012 : Surrey, England)
Conference location Surrey, England
Conference dates 3-7 Sept. 2012
Title of proceedings BMVC 2012 : Proceeding of the British Machine Vision Conference 2012
Editor(s) Bowden, Richard
Collomosse, John
Mikolajczyk, Krystian
Publication date 2012
Conference series British Machine Vision Conference
Start page 1
End page 11
Total pages 11
Publisher BMVA Press
Place of publication [Surrey, England]
Summary The problem of object recognition is of immense practical importance and potential, and the last decade has witnessed a number of breakthroughs in the state of the art. Most of the past object recognition work focuses on textured objects and local appearance descriptors extracted around salient points in an image. These methods fail in the matching of smooth, untextured objects for which salient point detection does not produce robust results. The recently proposed bag of boundaries (BoB) method is the first to directly address this problem. Since the texture of smooth objects is largely uninformative, BoB focuses on describing and matching objects based on their post-segmentation boundaries. Herein we address three major weaknesses of this work. The first of these is the uniform treatment of all boundary segments. Instead, we describe a method for detecting the locations and scales of salient boundary segments. Secondly, while the BoB method uses an image based elementary descriptor (HoGs + occupancy matrix), we propose a more compact descriptor based on the local profile of boundary normals’ directions. Lastly, we conduct a far more systematic evaluation, both of the bag of boundaries method and the method proposed here. Using a large public database, we demonstrate that our method exhibits greater robustness while at the same time achieving a major computational saving – object representation is extracted from an image in only 6% of the time needed to extract a bag of boundaries, and the storage requirement is similarly reduced to less than 8%.
ISBN 1901725464
Language eng
DOI 10.5244/C.26.85
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 E1.1 Full written paper - refereed
Copyright notice ©2010, BMVA Press
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058421

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.