Extracting dense features for visual correspondence with graph cuts

Abstract

We present a method for extracting dense features from stereo and motion sequences. Our dense feature is defined symmetrically with respect to both images, and it is extracted during the correspondence process, not in a separate preprocessing step. For dense feature extraction we use the graph cuts algorithm, recently shown to be a powerful optimization tool for vision. Our algorithm produces semi-dense answer, with very accurate results in areas where features are detected, and no matches in featureless regions. Unlike sparse feature based algorithms, we are able to extract accurate correspondences in some untextured regions, provided that there are texture cues on the boundary. Our algorithm is robust and does not require parameter tuning.