TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images
This addresses the problem of robust line detection for applications like autonomous driving, though it is incremental as it builds on existing line detection methods with added topological organization.
The paper tackles line segment detection in low-quality binary images by introducing TGGLines, a topological graph-guided approach that outperforms state-of-the-art methods both visually and quantitatively, while requiring only one adaptive parameter.
Line segment detection is an essential task in computer vision and image analysis, as it is the critical foundation for advanced tasks such as shape modeling and road lane line detection for autonomous driving. We present a robust topological graph guided approach for line segment detection in low quality binary images (hence, we call it TGGLines). Due to the graph-guided approach, TGGLines not only detects line segments, but also organizes the segments with a line segment connectivity graph, which means the topological relationships (e.g., intersection, an isolated line segment) of the detected line segments are captured and stored; whereas other line detectors only retain a collection of loose line segments. Our empirical results show that the TGGLines detector visually and quantitatively outperforms state-of-the-art line segment detection methods. In addition, our TGGLines approach has the following two competitive advantages: (1) our method only requires one parameter and it is adaptive, whereas almost all other line segment detection methods require multiple (non-adaptive) parameters, and (2) the line segments detected by TGGLines are organized by a line segment connectivity graph.