Abstract
A simple method is presented for detecting, localizing and recognizing classes of objects, that accommodates wide variation in an object's pose. The method utilizes a small two-dimensional template that is warped into an image, and converts localization to a one-dimensional sub-problem, with the search for a match between image and template executed by dynamic programming. The method recovers three of the six degrees of freedom of motion (2 translation, 1 rotation), and accommodates two more DOF in the search process (1 rotation, 1 translation), and is extensible to the final DOF. Experiments demonstrate that the method provides an efficient search strategy that outperforms normalized correlation. This is demonstrated in the example domain of face detection and localization, and is extended to more general detection tasks. An additional technique recovers a rough object pose from the match results, and is used in a two stage recognition experiment using maximization of mutual information.
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