2021 journal article

Learning Regional Attraction for Line Segment Detection

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 43(6), 1998–2013.

co-author countries: China 🇨🇳 United Kingdom of Great Britain and Northern Ireland 🇬🇧 United States of America 🇺🇸
author keywords: Image segmentation; Image edge detection; Detectors; Lattices; Machine learning; Electronic mail; Training; Line segment detection; low-level vision; deep learning
Source: Web Of Science
Added: June 10, 2021

This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.