Exploiting local intensity information in Chan–Vese model for noisy image segmentation

2013 ◽  
Vol 93 (9) ◽  
pp. 2709-2721 ◽  
Author(s):  
Linghui Liu ◽  
Li Zeng ◽  
Kuan Shen ◽  
Xiao Luan
Keyword(s):  
Image Segmentation ◽  
Noisy Image ◽  
Intensity Information ◽  
Local Intensity

Integrating guided filter into fuzzy clustering for noisy image segmentation

2018 ◽  
Vol 83 ◽  
pp. 235-248 ◽  
Author(s):  
Li Guo ◽  
Long Chen ◽  
C.L. Philip Chen ◽  
Jin Zhou
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Guided Filter ◽  
Noisy Image

Research on Underwater Polarization Image Segmentation Inspired by Biological Optic Nerve

2013 ◽  
Vol 347-350 ◽  
pp. 2178-2184
Author(s):  
Hui Bin Wang ◽  
Yu Rong Wu ◽  
Jie Shen ◽  
Zhe Chen
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Model ◽  
Neural Network Model ◽  
Underwater Image ◽  
The Neural Network ◽  
Intensity Information ◽  
Segmentation Methods ◽  
Polarization Image ◽  
Feedback Neural Network

Due to effects of the light by water and other particles, the quality of underwater image will degrade. The traditional underwater image segmentation methods based on intensity and spectrum have difficulty in determining boundary. Inspired by the visual system of mantis shrimps, this paper constructed a feedback neural network model, in which the parameters were optimized using machine learning method. Based on this model, we combine the polarization and intensity information to achieve the underwater polarization image segmentation. The results of experiment prove that the neural network model designed in this paper can improve the accuracy of underwater image segmentation.

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