Linear features extraction with an orientation constrained probabilistic hough transform

This study proposed a Hough Transform algorithm based on the probabilistic scheme termed as the Orientation Constrained Probabilistic Hough Transform. The orientation constraints for segmentation are applied to form compact and reliable sampling subsets. This process is subsequently followed by constrained searching. Numerical experiments are performed with both synthetic and real datasets indicate that the proposed method performs better than other algorithms in terms of correctness and omission rate. Moreover, computational time deemed necessary is much less than that for other algorithms.