Feature matching constrained by cross ratio invariance

摘要

The main aim of this work is to propose a new technique to solve the well-known feature correspondence problem for motion estimation. The problem is formulated as an optimization process whose energy function includes constraints based on projective invariance of cross-ratio of five coplanar points. Starting from some approximated correspondences, estimated by radiometric similarity, for features with high directional variance, optimal matches are obtained through an optimization technique. The new contribution of this work consists of a matching process, refining the raw measurements, based on an energy function minimization technique converging to an optimal solution for most of the features by taking advantage of some good initial guess, and in the use of cross ratio as geometrical invariant constraint to detect and correct the mismatches due to wrong radiometric similarity measures. Though the method is based on geometrical invariance of coplanar points, it is not required that all features have to be coplanar or to preprocess the images to detect the planar regions. Experimental results are presented for real and synthetic images, and the performance of the novel approach is evaluated on different image sequences and compared to well-known techniques.