“Feature Matching With Bounded Distortion” by Lipman, Yagev, Poranne, Jacobs and Basri

  • ©Yaron Lipman, Stav Yagev, Roi Poranne, David W. Jacobs, and Ronen Basri




    Feature Matching With Bounded Distortion

Session/Category Title: Surfaces, Deformation, and Correspondence




    We consider the problem of finding a geometrically consistent set of point matches between two images. We assume that local descriptors have provided a set of candidate matches, which may include many outliers. We then seek the largest subset of these correspondences that can be aligned perfectly using a nonrigid deformation that exerts a bounded distortion. We formulate this as a constrained optimization problem and solve it using a constrained, iterative reweighted least-squares algorithm. In each iteration of this algorithm we solve a convex quadratic program obtaining a globally optimal match over a subset of the bounded distortion transformations. We further prove that a sequence of such iterations converges monotonically to a critical point of our objective function. We show experimentally that this algorithm produces excellent results on a number of test sets, in comparison to several state-of-the-art approaches.


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