Papers › MCMLSD: A Dynamic Programming Approach to Line Segment Detection

MCMLSD: A Dynamic Programming Approach to Line Segment Detection

1 Jul 2017CVPR 2017 7archive 2025-07-28

Emilio J. Almazan, Ron Tal, Yiming Qian, James H. Elder

Prior approaches to line segment detection typically involve perceptual grouping in the image domain 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 dataset shows that the proposed MCMLSD method outperforms the state-of-the-art by a substantial margin.

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Tasks

Line Segment Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Line Segment Detection York Urban Dataset MCMLSD sAP10 9.2 #13 of 16 Archive leaderboard report
Line Segment Detection York Urban Dataset MCMLSD sAP5 7.2 #13 of 16 Archive leaderboard report
Line Segment Detection wireframe dataset MCMLSD sAP10 10.4 #8 of 10 Archive leaderboard report
Line Segment Detection wireframe dataset MCMLSD sAP5 7.6 #8 of 10 Archive leaderboard report

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