Object recognition using fast adaptive Hough transform

Abstract

A fast adaptive Hough transform (FAHT) approach is developed for detecting shapes which can be characterized by two parameters. This class of shapes includes both linear and circular image features. The method is based on identifying linear and circular segments in images by searching for clusters of evidence in two-dimensional parameter spaces. The FAHT differs from HT in the degree of freedom allowed in the placement and choice of shape of the window which defines the range of parameters under study at each resolution. This method is superior to that of HT implementation in both storage and computational requirements. The ideas of the FAHT are illustrated by tackling the problem of identifying linear segments in images by searching for clusters of evidence in two-dimensional parameter spaces. It is shown that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape. >