Learning Regional Attraction for Line Segment Detection

TL;DR

This paper introduces a novel regional attraction approach for line segment detection, transforming the problem into region coloring and attraction field mapping, leading to improved accuracy and robustness over existing methods.

Contribution

The paper proposes a new end-to-end framework that learns attraction field maps for line segment detection, effectively handling local ambiguities without relying on precise edge pixel detection.

Findings

Achieves an F-measure of 0.831 on the Wireframe dataset

Advances state-of-the-art performance by 10.3%

Demonstrates superior results on multiple datasets

Abstract

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.