A Hough Transform Algorithm with a 2D Hypothesis Testing Kernel

Abstract

In this paper we consider a Hough transform line finding algorithm in which the voting kernel is a smooth function of differences in both line parameters. The shape of the voting kernel is decided in terms of a hypothesis testing approach, and the shape is adjusted to give optimal results. We show that this new kernel is robust to changes in the distribution of the underlying noise and the implementation is very fast, taking typically 2-3 s on a Sparc 2 workstation for a 256 × 256 image.