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Beyond one-to-one feature correspondence: The need for many-to-many matching and image abstraction

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1 Author(s)
Sven Dickinson ; Department of Computer Science, University of Toronto, USA

Summary form only given: In this paper briefly review three formulations of the many-to-many matching problem as applied to model acquisition, model indexing, and object recognition. In the first scenario, I will describe the problem of learning a prototypical shape model from a set of exemplars in which the exemplars may not share a single local feature in common. We formulate the problem as a search through the intractable space of feature combinations, or abstractions, to find the "lowest common abstraction" that is derivable from each input exemplar. This abstraction, in turn, defines a many-to-many feature correspondence among the extracted input features.

Published in:

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Date of Conference:

20-25 June 2009