PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Part-based Probabilistic Point Matching using Equivalence Constraints
Graham McNeill and Sethu Vijayakumar
In: Advances in Neural Information Processing Systems 19, Dec 2006, Vancouver, Canada.

Abstract

Correspondence algorithms typically struggle with shapes that display part-based variation. We present a probabilistic approach that matches shapes using independent part transformations, where the parts themselves are learnt during matching. Ideas from semi-supervised learning are used to bias the algorithm towards finding ‘perceptually valid’ part structures. Shapes are represented by unlabeled point sets of arbitrary size and a background component is used to handle occlusion, local dissimilarity and clutter. Thus, unlike many shape matching techniques, our approach can be applied to shapes extracted from real images. Model parameters are estimated using an EM algorithm that alternates between finding a soft correspondence and computing the optimal part transformations using Procrustes analysis.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:3690
Deposited By:Sethu Vijayakumar
Deposited On:14 February 2008