MCMLSD: A Probabilistic Algorithm and Evaluation Framework for Line Segment Detection

Abstract

Traditional approaches to line segment detection typically involve perceptual grouping in the image domain and/or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a first stage lines are detected using a global probabilistic Hough approach. In the second stage each detected line is analyzed in the image domain to localize the line segments that generated the peak in the Hough map. By limiting search to a line, the distribution of segments over the sequence of points on the line can be modeled as a Markov chain, and a probabilistically optimal labelling can be computed exactly using a standard dynamic programming algorithm, in linear time. The Markov assumption also leads to an intuitive ranking method that uses the local marginal posterior probabilities to estimate the expected number of correctly labelled points on a segment. To assess the resulting Markov Chain Marginal Line Segment Detector (MCMLSD) we develop and apply a novel quantitative evaluation methodology that controls for under-and over-segmentation. Evaluation on the YorkUrbanDB and Wireframe datasets shows that the proposed MCMLSD method outperforms prior traditional approaches, as well as more recent deep learning methods.

Line segment detection algorithm in image extraction improvement study

“…The improved IM-LSD algorithm and LSD algorithm in this paper, the Kanade optical flow algorithm based on the rapid detection technology of panoramic images in literature [16], the MIDV algorithm based on Hough transform in literature [23], and the Linelet algorithm based on the optimal combination of atomic and ion lines in literature [24], are introduced. As well as the MCMLSD algorithm based on perceptual grouping and global accumulation in Hough domain in literature [25], the six algorithms were compared in C\C++ environment. IM-LSD, Kanade, MIDV, MCMLSD, Linelet and LSD algorithms in the same dataset are shown in Fig.…”