Flexible Template Matching For Autonomous Classification

TLDR: Flexible template matching as mentioned in this paper automatically brings the unknown target image and each one, in turn of a set of class-defining template images (one per class) into mutual registration, without requiring any prior knowledge of the differences of view that may exist between them.

Abstract: The target recognition problem is complicated by the fact that target is doubly "unknown". The sensed image is obviously a function of target class: a fundamental problem of target recognition has been that the form of the sensed image is also a strong function of target "geometry". The differences of class, upon which classification necessarily depends, may therefore be com-pletely swamped by the irrelevant differences of view. Human recognition of targets is generally unaffected by this problem. A new method of Automatic Target Recognition, called Flexible Template Matching, is described. It eliminates "geometry" from the recognition problem, by means of a new registration algorithm. This automatically brings the unknown target image and each one, in turn, of a set of class-defining template images (one per class) into mutual registration, without requiring any prior knowledge of the differences of view that may exist between them. The elimination of all irrelevant differences of view (scale, position, rotation, aspect, etc) allows for an optimum match-decision to identify the one true template, based upon computation (using Bayes Formula) of the probability, for each template, that the observed match differences are a typical sample of the match-differences known to occur in a (registered) true match of the target and its template. Since the range and aspect of the target are provided as a by-product of the registration action, the match-error statistics used can be selected according to the observed position and orientation of the target.