Papers › TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line...
TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images
Liping Yang, Joshua Driscol, Ming Gong, Katie Slack, Wenbin Zhang, Shujie Wang, Catherine G. Potts
Line detection is a classic and essential problem in image processing, computer vision and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recognition and art design), indoor mapping, and important societal challenges (e.g., sea ice fracture line extraction from satellite imagery). Many line detection algorithms and methods have been developed, but robust and intuitive methods are still lacking. In this paper, we proposed and implemented a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our TGGLinesPlus algorithm. We benchmarked our algorithm with five classic and state-of-the-art line detection methods and evaluated the benchmark results qualitatively and quantitatively, the results demonstrate the robustness of TGGLinesPlus.
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