Maximum-likelihood image matching

Abstract

Image-matching applications, such as tracking and stereo, commonly use the sum-of-squared-difference (SSD) measure to determine the best match. However, this measure is sensitive to outliers and is not robust to template variations. Alternative measures have also been proposed that are more robust to these issues. We improve upon these using a probabilistic formulation for image matching in terms of maximum-likelihood estimation that can be used for both edge template matching and gray-level image matching. This formulation generalizes previous edge-matching methods based on distance transforms. We apply the techniques to stereo matching and feature tracking. Uncertainty estimation techniques allow feature selection to be performed by choosing features that minimize the localization uncertainty.