Chapter 8 – Template Matching

Publisher Summary This chapter explores the template matching. Template matching involves defining a measure or a cost to find the “similarity” between the (known) reference patterns and the (unknown) test pattern by performing the matching operation. It finds its application in speech recognition, in automation using robot vision, in motion estimation for video coding, and in image database retrieval systems. The chapter explores the problem of string pattern matching and then deals with the scene analysis and shape recognition problems. The tasks, although they share the same goal, require different tools because of their different nature. Measures based on optimal path searching techniques and measures based on correlation are discussed. One section discusses a category of template matching, where the involved patterns consist of strings of identified symbols or feature vectors (string patterns). Topics of deformable template model and content-based information retrieval are discussed. Dynamic programming and the Viterbi algorithm are presented in the chapter and then applied to speech recognition. Correlation matching and the basic philosophy behind deformable template matching are also presented.

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