Worthington, P L and Hancock, E R orcid.org/0000-0003-4496-2028 (2001) Object recognition using shape-from-shading. IEEE Transactions on Pattern Analysis and Machine Intelligence. pp. 535-542. ISSN 0162-8828
Abstract
This paper investigates whether surface topography information extracted from intensity images using a recently reported shape-from-shading (SFS) algorithm can be used for the purposes of 3D object recognition. We consider how curvature and shape-index information delivered by this algorithm can be used to recognize objects based on their surface topography. We explore two contrasting object recognition strategies. The first of these is based on a low-level attribute summary and uses histograms of curvature and orientation measurements. The second approach is based on the structural arrangement of constant shape-index maximal patches and their associated region attributes. We show that region curvedness and a string ordering of the regions according to size provides recognition accuracy of about 96 percent. By polling various recognition schemes. including a graph matching method. we show that a recognition rate of 98-99 percent is achievable.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright © 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
Keywords: | shape-from-shading,object recognition,shape-index,histograms,constant shape-index maximal patches,graph-matching,VISCOSITY SOLUTIONS,SOLID SHAPE,IMAGES,PERCEPTION,DISTANCE,SET |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Repository Officer |
Date Deposited: | 21 Feb 2007 |
Last Modified: | 16 Oct 2024 11:58 |
Published Version: | https://doi.org/10.1109/34.922711 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/34.922711 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:1989 |