An Edge-Weighted Centroidal Voronoi Tessellation Model for Image Segmentation

2009 ◽  
Vol 18 (8) ◽  
pp. 1844-1858 ◽  
Author(s):  
Jie Wang ◽  
Lili Ju ◽  
Xiaoqiang Wang
Keyword(s):  
Image Segmentation ◽  
Voronoi Tessellation ◽  
Centroidal Voronoi Tessellation

scholarly journals A fuzzy edge-weighted centroidal Voronoi tessellation model for image segmentation

2016 ◽  
Vol 71 (11) ◽  
pp. 2272-2284 ◽  
Author(s):  
Xiaochuan Fan ◽  
Lili Ju ◽  
Xiaoqiang Wang ◽  
Song Wang
Keyword(s):  
Image Segmentation ◽  
Voronoi Tessellation ◽  
Centroidal Voronoi Tessellation

Mesh clustering by approximating centroidal Voronoi tessellation

2009 ◽  
Author(s):  
Fengtao Fan ◽  
Fuhua (Frank) Cheng ◽  
Conglin Huang ◽  
Yong Li ◽  
Jianzhong Wang ◽  
...  
Keyword(s):  
Voronoi Tessellation ◽  
Centroidal Voronoi Tessellation

scholarly journals Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

2020 ◽  
Vol 12 (24) ◽  
pp. 4115
Author(s):  
Xiaoli Li ◽  
Jinsong Chen ◽  
Longlong Zhao ◽  
Shanxin Guo ◽  
Luyi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Noise Immunity ◽  
Voronoi Tessellation ◽  
Spectral Characteristics ◽  
Remote Sensing Image ◽  
Effective Characteristics ◽  
Spatial Distance ◽  
Remote Sensing Images ◽  
Distance Weighted

The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characteristics. In view of the difficulty of balancing the noise immunity and effective characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed for remote sensing image segmentation. The distance between pixels and seed points in Voronoi tessellation is established by the adaptive weighting of spatial distance and spectral distance. The weight coefficient used to control the influence intensity of spatial distance is defined by a monotone decreasing function. Following the fuzzy clustering framework, a fuzzy segmentation model with Kullback–Leibler (KL) entropy regularization is established by using multivariate Gaussian distribution to describe the spectral characteristics and Markov Random Field (MRF) to consider the neighborhood effect of sub-regions. Finally, a series of parameter optimization schemes are designed according to parameter characteristics to obtain the optimal segmentation results. The proposed algorithm is validated on many multispectral remote sensing images with five comparing algorithms by qualitative and quantitative analysis. A large number of experiments show that the proposed algorithm can overcome the complex noise as well as better ensure effective characteristics.

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