Maximum-likelihood template matching

Abstract

In image matching applications such as tracking and stereo matching, it is common to use the sum-of-squared-differences (SSD) measure to determin the best match for an image template. However, this measure is sensitive to outliers and is not robust to template variations. We describe a robust measure and efficient search strategy for template matching with a binary or greyscale temolate using a maximum-likelihood formulation. In addition to subpixel localization and uncertainty estimation, these techniques allow optimal feature selection based on minimizing the localization uncertainty. We examine the use of these techniques for object recognition, stereo matching, feature selection and tracking.

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