Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component.
It is different from "Pattern Recognition" that is recognized on the basis of general patterns and larger collections of related samples. It specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Pattern recognition primarily cares about the representation. It faces the challenge to deal with images of different sizes, orientations and illumination conditions, or with time signals of arbitrary length and varying offset. Pattern recognition includes preprocessing procedures to normalize observations, to deal with invariants and to define proper features and distance measures. Once a proper representation is found, learning procedures become of interest and the results of Machine Learning can be applied.
Pattern recognition was a term popular in the 70s and 80s. The emphasis was on getting a computer program to do something “smart” like recognize the character "3". And it really took a lot of cleverness and intuition to build such a program. Just think of "3" vs "B" and "3" vs "8". Back in the day, it didn’t really matter how you did it as long as there was no human-in-a-box pretending to be a machine. So if your algorithm would apply some filters to an image, localize some edges, and apply morphological operators, it was definitely of interest to the pattern recognition community. Optical Character Recognition grew out of this community and it is fair to call “Pattern Recognition” as the “Smart" Signal Processing of the 70s, 80s, and early 90s. Decision trees, heuristics, quadratic discriminant analysis, etc all came out of this era.
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Like in the discussion of Artificial Intelligence and Pattern matching we find here once more an opposition of directions. Pattern Recognition starts with real world problems and finds at some moment the need to use Machine Learning results. Machine Learning from its side starts with a well-defined machine environment and a given representation of external problems, but then starts to search for applications to validate its results. In short, Pattern Recognition studies problems in need for a solution.