TGGLinesPlus: A robust topological graph-guided computer vision
  algorithm for line detection from images
Liping Yang Joshua Driscol Ming Gong 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 also benchmarked our algorithm with five classic and
state-of-the-art line detection methods and the results demonstrate the
robustness of TGGLinesPlus. We hope our open-source implementation of
TGGLinesPlus will inspire and pave the way for many applications where spatial
science matters.