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

TL;DR

This paper introduces MCMLSD, a probabilistic algorithm that combines global Hough detection with local Markov chain analysis to accurately detect line segments, outperforming traditional and deep learning methods.

Contribution

The paper presents a novel probabilistic line segment detection algorithm that integrates Hough and image domain analysis with an efficient dynamic programming approach.

Findings

Outperforms prior traditional methods.

Outperforms recent deep learning approaches.

Provides a new evaluation framework for segmentation quality.

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.