A hierarchical combined feature and area-based stereo matching algorithm

This paper develops a novel computational framework for an accurate, robust, and efficient stereo analysis, which is a hierarchical scheme based on the combination of feature- and area-based matching, called hierarchical hybrid matching algorithm (HHM). The HHM can inherit the accuracy of the feature-based approaches, simplify the procedure of feature matching and produce robust and dense disparity fields. What is more, the HHM can reduce the dependency on the quantity and quality of the features extracted from the original images to a great degree. On the other hand, unlike existing area-based approaches the HHM can generate global disparity fields, produce more precise disparity estimates on the edge of the objects and avoid searching blindly in a wide range. Experimentations are carried out on a group of widely used stereo images.