Weak boundary preserved superpixel segmentation based on directed graph clustering

2018 ◽  
Vol 65 ◽  
pp. 231-239 ◽  
Author(s):  
Li Xu ◽  
Bing Luo ◽  
Zheng Pei
2015 ◽  
Vol 25 (11) ◽  
pp. 1735-1748 ◽  
Author(s):  
Fanman Meng ◽  
Hongliang Li ◽  
Shuyuan Zhu ◽  
Bing Luo ◽  
Chao Huang ◽  
...  

Author(s):  
Xiao Zhang ◽  
Bosen Lian ◽  
Frank L. Lewis ◽  
Yan Wan ◽  
Daizhan Cheng

2014 ◽  
Vol 36 (8) ◽  
pp. 1704-1713 ◽  
Author(s):  
Ye WU ◽  
Zhi-Nong ZHONG ◽  
Wei XIONG ◽  
Luo CHEN ◽  
Ning JING

2019 ◽  
Vol 5 (6) ◽  
pp. 57 ◽  
Author(s):  
Gang Wang ◽  
Bernard De Baets

Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection.


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