Title :
NETLines: Recovering line-networks via gradient-based line segments refinement
Author :
Xiaohu Lu;Jian Yao;Kai Li;Li Li;Kao Zhang;Jinge Tu
Author_Institution :
School of Remote Sensing and Information Engineering, Wuhan University, Hubei, China
Abstract :
In this paper, we propose a novel line segment detector, named as NETLines, which can produce a set of accurate line segments and a set of node-connected line-networks formed by connection of the line segments and the image boundary. Based on the line segments generated by other line segment detectors (e.g., EDLines [1]) on an edge map, the proposed algorithm efficiently makes use of the gradient map of the original image instead of its edge map to extend and refine the line segments. The line-networks are constructed with the extended and refined line segments and a set of nodes generated by connecting of the line segments. Furthermore, the line segments and line-networks are optimally supplemented and refined by linking and merging the line segments. Experimental results on a set of natural images illustrate the proposed NETLines produces more accurate and complete line segments compared with state-of-the-art line segments detectors.
Keywords :
"Image segmentation","Image edge detection","Detectors","Joining processes","Merging","Clutter","Robustness"
Conference_Titel :
Information and Automation, 2015 IEEE International Conference on
DOI :
10.1109/ICInfA.2015.7279256