Incremental local Hough Transform for line segment extraction

Abstract

Although global voting schemes, such as the Hough Transform (HT), have been widely used to robustly detect lines in images, they fail when the line segments at hand are short, particularly if the underlying edge maps are cluttered. Line segment detection in these scenarios has been addressed using local methods, which lack robustness to missing data (interrupted lines) and typically fail when line segments cross. We propose a new method that tackles these problems: first, rough estimates of plural candidate directions at each edge point are obtained through a directional local HT; then, the parameters determining the line segments are globally estimated by maximizing a quality measure that depends on all the edge points. Our experiments illustrate that the proposed method outperforms current methods in challenging situations.