An Optimizing Line Finder Using a Hough Transform Algorithm

In this paper we present an optimization algorithm for locating peaks in the accumulator of the Hough algorithm with robust voting kernel. We present a detailed discussion of the accuracy that can be achieved by locating these peaks in the accumulator, and show that the error bounds on the estimates of line parameters are always within those based upon least squares. This arises from the robust nature of the voting kernel. We describe the optimization algorithm in some detail since the shape of the peaks in the standard parameter space for straight lines are sinusoidal ridges. Standard approaches therefore fail, but the method described is shown to be robust from the experimental results presented. Some discussion of post-processing is also made, in which the shortcomings of standard Hough techniques, splitting long lines across parameter bins, can be remedied. We also discuss the use of a confidence measure in the line parameters based upon the value of the accumulator, and show that this is related to the mean squared distance from the line of the edge pixels associated with it. Finally, we present results produced by this optimizing Hough technique on a disparate set of images, with various application areas in mind, to demonstrate the versatility of the method and the accuracy that can be achieved at little computational overhead.