Multiview fusion for canonical view generation based on homography constraints
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
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International Journal of Computer Vision
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2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling
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IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Combining different types of scale space interest points using canonical sets
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WSEAS Transactions on Signal Processing
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ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
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International Journal of Computer Vision
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MM '11 Proceedings of the 19th ACM international conference on Multimedia
Stable bounded canonical sets and image matching
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Semantics-driven approach for automatic selection of best views of 3D shapes
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Image collection summarization via dictionary learning for sparse representation
Pattern Recognition
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Given a collection of sets of 2-D views of 3-D objects and a similarity measure between them, we present a method for summarizing the sets using a small subset called a bounded canonical set (BCS), whose members best represent the members of the original set. This means that members of the BCS are as dissimilar from each other as possible, while at the same time being as similar as possible to the non-BCS members. This paper will extend our earlier work on computing canonical sets [Approximation of canonical sets and their application to 2d view simplification] in several ways: by omitting the need for a multi-objective optimization, by allowing the imposition of cardinality constraints, and by introducing a total similarity function. We evaluate the applicability of BCS to view selection in a view-based object recognition environment.