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 points of an object in two views. 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 standard algorithms.
['Bernardo Rodrigues Pires', 'José M. F. Moura', 'João Manuel Freitas Xavier']
LASIC: A model invariant framework for correspondence