IDIAP Finding Lines under Bounded

A new algorithm for nding lines in images under a bounded error noise model is described. The algorithm is based on a hierarchical and adaptive subdivision of the space of line parameters, but, unlike previous adaptive or hierarchical line nders based on the Hough transform, measures errors in image space and thereby guarantees that no solution satisfying the given error bounds will be lost. In addition, the algorithm can nd interpretations of all the lines in the image that satisfy the constraint that each image feature supports at most one line hypothesis{a constraint that is often useful to impose in practice. The algorithm can be extended to compute the probabilistic Hough transform and the generalized Hough transform a variety of statistical error models eeciently.