Window Matching using Sparse Templates

This report describes a novel window matching technique. We perform window matching by transforming image data into sparse features, and apply a computationally efficient matching technique in the sparse feature space. The gain in execution time for the matching is roughly 10 times compared to full window matching techniques such as SSD, but the total execution time for the matching also involves an edge filtering step. Since the edge responses may be used for matching of several regions, the proposed matching technique is increasingly advantageous when the number of regions to keep track of increases, and when the size of the search window increases. The technique is used in a real-time ego-motion estimation system in the WITAS project. Ego-motion is estimated by tracking of a set of structure points, i.e. regions that do not have the aperture problem. Comparisons with SSD, with regard to speed and accuracy are made.