Compression-based template matching

Textual image compression is a method of both lossy and lossless image compression that is particularly effective for images containing repeated sub-images, notably pages of text. This paper addresses the problem of pattern comparison by using an information or compression based approach. Following Mohiuddin et al. ( 1984), the authors use the amount of uncertainty or entropy between marks as the criterion for the matching process. The entropy model they use is the context-based compression model proposed by Langdon and Rissanen (1981) and further developed by Moffat (1991). There are two principal issues to investigate when studying template matching methods: their susceptibility to different kinds of noise, and how they respond to errors in the initial registration. Because of the computation-intensive nature of the comparison operation, many schemes have been devised to pre-filter or screen the marks in advance to determine those that will surely fail the match. They present a novel method of screening which uses a quad-tree decomposition and finds local centroids at each tree level