Towards a generalized template matching algorithm for pictorial pattern recognition

This paper is concerned with the use of nonlinear regression analysis as a means for achieving pictorial pattern recognition. The object to be identified is presumed to be represented by a set of image plane coordinates representing points associated with its boundary. Regression analysis techniques are then used to produce a sequence of computationally generated templates for various types of objects. The best fitting template type determines the classification of the object. Experimental results relating to a four class problem involving ellipses, rectangles, concave cresents, and convex cresents are presented.