On the computation of the Circle Hough Transform by a GPU rasterizer

This paper presents an alternative for a fast computation of the Hough transform by taking advantage of commodity graphics processors that provide a unique combination of low cost and high performance platforms for this sort of algorithms. Optimizations focus on the features of a GPU rasterizer to evaluate, in hardware, the entire spectrum of votes for circle candidates from a small number of key points or seeds computed by the GPU vertex processor in a typical CPU manner. Number of votes and fidelity of their values are analyzed within the GPU using mathematical models as a function of the radius size for the circles to be detected and the resolution for the texture storing the results. Empirical results validate the output obtained for a much faster execution of the Circle Hough Transform (CHT): On a 1024 × 1024 sample image containing 20 circles of r = 50 pixels, the GPU accelerates the code an order of magnitude and its rasterizer contributes with an additional 4× factor for a total speed-up greater than 40× versus a baseline CPU implementation.