Accurate and Efficient Curve Detection in Images: The Importance Sampling Hough Transform

摘要

The Hough transform is a well known technique for detecting parametric curves in images. We place a particular group of Hough transforms, the probabilistic Hough transforms, in the framework of importance sampling. This framework suggests a way in which probabilistic Hough transforms can be improved: by specifying a target distribution and weighting the sampled parameters accordingly to make identification of curves easier. We investigate the use of clustering techniques to simultaneously identify multiple curves in an image. We also use probabilistic arguments to develop stopping conditions for the algorithm. The resulting methodology is called the Importance Sampling Hough Transform (ISHT). We apply our method to both simulated and real data, and compare its performance with that of two much used versions of the Hough transform: the standard Hough transform and the randomized Hough transform. In our experiments, it is more accurate than either of these common methods, and it is faster than the randomized Hough transform.