A practical, globally optimal algorithm for geometric matching under uncertainty

Geometric matching under uncertainty is a long-standing problem in computer vision. This paper presents a simple and efficient algorithm for finding globally optimal solutions to geometric matching problems under a wide variety of allowable transformations (translations, isometries, equiform transformations, others) and a wide variety of allowable feature types (point features, oriented point features, line features, line segment features, etc.). The algorithm only requires an implementation of the forward transformation (model-to-image) and an error model to be supplied. Benchmarks and comparisons of the algorithm in comparison with alignment and Hough transform methods are presented.