Gradient angle histograms for efficient linear hough transform

Non-collinear edge pixels are equivalent to noise for the linear Hough transform (LHT). Existing methods that reduce the number of points for Hough voting are based on random and/or probabilistic selection. Such methods select both collinear and noisy pixels, thereby incurring unwanted computational costs. In this paper, we propose a novel gradient angle histogram based technique to generate modified straight line edge map (SLEM), which largely retains the straight line edges and eliminates noisy edge pixels. A block-based SLEM generation is proposed to increase the robustness of straight line extraction and validated on test images. Further, effect of varying block sizes on accuracy of straight line detection is studied and appropriate block settings are derived. The proposed gradient angle histogram based method reduces the number of edge pixels by as much as 85%.