An inverse voting algorithm for Hough Transform

In this paper, we propose a new method for curve detection based on 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 in the parameter space into a parameter optimization problem. This leads to substantial savings, 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.