LASIC: A model invariant framework for correspondence

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LASIC: A model invariant framework for correspondence
In this paper we address two closely related problems. The first is the object detection problem, i.e., the automatic decision of whether a given image represents a known object or not. The second is the correspondence problem, i.e., the automatic matching of the features of an object in two views. We use a feature-based approach for both problems. In the first problem, we assume object rigidity and model the distortions by a linear shape model. To solve the decision problem, we derive the uniformly most powerful (UMP) hypothesis test that is invariant to the linear shape model. We use the UMP statistic to formulate the correspondence problem in a model invariant framework. We show that it is equivalent to a quadratic maximization on the space of permutation matrices. We derive LASIC, an iterative computationally feasible solution to the quadratic maximization problem for the particular case where the linear shape model is the affine model. Simulations benchmark LASIC against two stan...
Bernardo Rodrigues Pires, João Xavier, Jos&
Added 20 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2008
Where ICIP
Authors Bernardo Rodrigues Pires, João Xavier, José M. F. Moura
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