Matching feature point sets lies at the core of many approaches to object recognition. We present a framework for nonrigid matching thatbegins with a skeleton module, affine point matching, and then integrates multiple features to improve correspondence and develops an object representation based on spatial regions to model local transformations.

Matching feature point sets lies at the core of many approaches to object recognition. We present a framework for nonrigid matching that begins with a skeleton module, affine point matching, and then integrates multiple features to improve correspondence and develops an object representation based on spatial regions to model local transformations.

A fundamental open problem in computer vision-determining pose and correspondence between two sets of points in spaceis solvedwith a novel, robust and easily implementable algorithm. The technique works on noisy point sets that may be of unequal sizes and may differ by nonrigid transformations. A 2D variation calculatesthe pose between point sets related by an affine transformation-translation, rotation, scale and shear. A 3D to 3D variation calculates translation and rotation. An objective describing theproblem is derived from Mean field theory. The objective is minimized with clocked (EMlike) dynamics. Experiments with both handwritten and synthetic data provide empirical evidence for the method. 1 Introduction

A fundamental open problem in computer vision-determining pose and correspondence between two sets of points in spaceis solved with a novel, robust and easily implementable algorithm. The technique works on noisy point sets that may be of unequal sizes and may differ by nonrigid transformations. A 2D variation calculates the pose between point sets related by an affine transformation-translation, rotation, scale and shear. A 3D to 3D variation calculates translation and rotation. An objective describing the problem is derived from Mean field theory. The objective is minimized with clocked (EMlike) dynamics. Experiments with both handwritten and synthetic data provide empirical evidence for the method. 1 Introduction