Bayesian classification for the Statistical Hough transform
We have introduced the statistical Hough transform that extends the standard Hough transform by using a kernel mixture as a robust alternative to the 2 dimensional accumulator histogram. This work develops further this framework by proposing a Bayesian classification scheme to associate the spatial coordinates (x, y) to one particular class defined in the Hough space (¿, ¿). In a first step, we segment the Hough space into meaningful classes. Then using the inverse Radon transform, we backproject the different classes into the image space. We illustrate our approach on a synthetic image and on real images.