Improved watershed algorithm for color image segmentation

2009 ◽  
Author(s):  
Hong-bo Tan ◽  
Zhi-qiang Hou ◽  
Xiao-chun Li ◽  
Rong Liu ◽  
Wei-wu Guo
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Watershed Algorithm

Color Image Segmentation in Both Feature andImage Spaces

2006 ◽  
pp. 209-227 ◽  
Author(s):  
Shengyang Dai ◽  
Yu-Jin Zhang
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Feature Space ◽  
Image Space ◽  
Color Image Segmentation ◽  
Watershed Algorithm ◽  
Random Field Theory ◽  
Irregular Shapes ◽  
Minimization Algorithms ◽  
A New Technique

One critical problem in image segmentation is how to explore the information in both feature and image space and incorporate them together. One approach in this direction is reported in this chapter. Watershed algorithm is traditionally applied on image domain but it fails to capture the global color distribution information. A new technique is to apply first the watershed algorithm in feature space to extract clusters with irregular shapes, and then to use feature space analysis in image space to get the final result by minimizing a global energy function based on Markov random field theory. Two efficient energy minimization algorithms: Graph cuts and highest confidence first (HCF) are explored under this framework. Experiments with real color images show that the proposed two-step segmentation framework is efficient and has been successful in various applications.

Roughness Measure Approach to Color Image Segmentation

2010 ◽  
Vol 36 (6) ◽  
pp. 807-816 ◽  
Author(s):  
Xiao-Dong YUE ◽  
Duo-Qian MIAO ◽  
Cai-Ming ZHONG
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation

Color image segmentation based on mean shift and multi-feature fusion

2009 ◽  
Vol 29 (8) ◽  
pp. 2074-2076
Author(s):  
Hua LI ◽  
Ming-xin ZHANG ◽  
Jing-long ZHENG
Keyword(s):  
Image Segmentation ◽  
Feature Fusion ◽  
Color Image ◽  
Mean Shift ◽  
Color Image Segmentation
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