Feature Integration and Selection for Pixel Correspondence

Pixel correspondence is an important problem in stereo vision, motion, structure from motion, etc. Several procedures have been proposed in the literature for this problem, using a variety of image features to identify the corresponding features. Di erent features work well under di erent conditions. An algorithm that can seamlessly integrate multiple features in a exible manner can combine the advantages of each. We propose a framework to combine heterogenous features, each with a di erent measure of importance, into a single correspondence computation in this paper. We also present an unsupervised procedure to select the optimal combination of features for a given pair of images by computing the relative importances of each feature. A unique aspect of our framework is that it is independent of the speci c correspondence algorithm used. Optimal feature selection can be done using any correspondence mechanism that can be extended to use multiple features. We also present a few examples that demonstrate the e ectiveness of the feature selection framework.