论文标题
TGGLines：低质量二进制图像的强大拓扑图指导线段探测器
TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images
论文作者
论文摘要
线段检测是计算机视觉和图像分析中的重要任务，因为它是高级任务的关键基础，例如形状建模和自动驾驶的道路车道线检测。我们为低质量二进制图像中的线段检测提供了强大的拓扑图指导方法（因此，我们称其为tgglines）。由于图形引导方法，TGGLines不仅检测线段，而且还通过线段连接图组织了段，这意味着捕获并存储了检测到的线段的拓扑关系（例如，交点，隔离线段）；而其他线探测器仅保留一系列松散的线段。我们的经验结果表明，TGGLINE在视觉上和定量上都优于最先进的线段检测方法。此外，我们的TGGLINE方法具有以下两个竞争优势：（1）我们的方法仅需要一个参数，并且是自适应的，而几乎所有其他线段段检测方法都需要多个（非自适应）参数，（2）由TGGLines检测到的线段由TGGLINE检测到的线段由线段连接图组成。
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.
