Curve Detection Using Inverse Hough Transform as a Parameter Optimization Problem

Abstract

The Hough Transform is an elegant way of extracting global features like parametric curves from binary edge images, however, its large computation and memory requirements prevent it from being used for practical computer vision tasks. This paper presents a new approach to detect parametric curves using the Inverse Hough Transform. The key idea of this method is to make the voting process on the image space instead of that on the parameter space in the conventional method, then convert the local peak detection problem into a parameter optimization problem. This leads to substantial saving, not only in storage requirements but also in the amount of calculation required. The experimental results and qualitative analysis showed that in comparison with the conventional Hough Transform methods, the new method has advantages of high speed, small storage, arbitrary parameter range and high parameter resolution.