Maximum-Likelihood Template Matching

Conference on Computer Vision and Pattern Recognition , 2000 . Maximum - Likelihood Template Matching

Maximum-Likelihood Image Matching

ÐImage 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 term...

Heeding More Than the Top Template

We present a method of classifying a pattern using information furnished by a ranked list of templates, rather than just the best matching template. We propose a parsimonious model to compute the class-conditional likelihood of a list of templates ranked on the basis of their match scores. We discuss the estimation of parameters used in the model. The results of maximum likelihood classificatio...

New Methods for Template Selection and Compression in Continuous Speech Recognition

We propose a maximum likelihood method for selecting template representatives, and in order to include more information in the selected template representatives, we further propose to create compressed template representatives by Gaussian mixture model (GMM) merging algorithm. A Kullback-Leibler (KL) divergence based local distance is proposed for Dynamic Time Warping (DTW) in template matching...

Extended Lucas-Kanade Tracking

The Lucas-Kanade (LK) method is a classic tracking algorithm exploiting target structural constraints thorough template matching. Extended Lucas Kanade or ELK casts the original LK algorithm as a maximum likelihood optimization and then extends it by considering pixel object / background likelihoods in the optimization. Template matching and pixel-based object / background segregation are tied ...