A template polynomial approach for image processing and visual recognition

Abstract

A polynomial approach to the representation of gray images for machine vision is described. An algebraic system is developed where a polynomial in two variables with real coefficients represents a gray image and it is shown that most of the standard image processing tasks like smoothing, edge detection, rotation and magnification can be done by operating certain polynomials called template polynomials. This method is also applied to connected component labelling, shape decomposition, template matching, and the skeletonization of a gray image without a priori thresholding. A technique is developed to decompose a template and do parallel processing.