Part-based probabilistic point matching

Graham McNeill; S. Vijayakumar

doi:10.1109/ICPR.2006.916

Part-based probabilistic point matching

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

We present a probabilistic technique for matching partbased shapes. Shapes are represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not pose a problem. Occlusions and significant dissimilarities between shapes are explained by a 'background model' and hence, their impact on the overall match is limited. Using a part-based model, we can successfully match shapes which differ as a result of independent part transformations a form of variation common amongst real objects of the same class. A greedy algorithm that learns the parts sequentially can be used to estimate the number of parts and the initial parameters for the main algorithm.

Original language	English
Title of host publication	Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	382-386
Number of pages	5
Volume	2
ISBN (Print)	0-7695-2521-0
DOIs	https://doi.org/10.1109/ICPR.2006.916
Publication status	Published - 2006

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10.1109/ICPR.2006.916

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1699225

Cite this

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title = "Part-based probabilistic point matching",

abstract = "We present a probabilistic technique for matching partbased shapes. Shapes are represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not pose a problem. Occlusions and significant dissimilarities between shapes are explained by a 'background model' and hence, their impact on the overall match is limited. Using a part-based model, we can successfully match shapes which differ as a result of independent part transformations a form of variation common amongst real objects of the same class. A greedy algorithm that learns the parts sequentially can be used to estimate the number of parts and the initial parameters for the main algorithm.",

author = "Graham McNeill and S. Vijayakumar",

year = "2006",

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language = "English",

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booktitle = "Pattern Recognition, 2006. ICPR 2006. 18th International Conference on",

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address = "United States",

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AU - McNeill, Graham

AU - Vijayakumar, S.

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N2 - We present a probabilistic technique for matching partbased shapes. Shapes are represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not pose a problem. Occlusions and significant dissimilarities between shapes are explained by a 'background model' and hence, their impact on the overall match is limited. Using a part-based model, we can successfully match shapes which differ as a result of independent part transformations a form of variation common amongst real objects of the same class. A greedy algorithm that learns the parts sequentially can be used to estimate the number of parts and the initial parameters for the main algorithm.

AB - We present a probabilistic technique for matching partbased shapes. Shapes are represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not pose a problem. Occlusions and significant dissimilarities between shapes are explained by a 'background model' and hence, their impact on the overall match is limited. Using a part-based model, we can successfully match shapes which differ as a result of independent part transformations a form of variation common amongst real objects of the same class. A greedy algorithm that learns the parts sequentially can be used to estimate the number of parts and the initial parameters for the main algorithm.

U2 - 10.1109/ICPR.2006.916

DO - 10.1109/ICPR.2006.916

M3 - Conference contribution

SN - 0-7695-2521-0

VL - 2

SP - 382

EP - 386

BT - Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Part-based probabilistic point matching

Abstract / Description of output

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