A Texture Image Segmentation Method Using Neural Networks and Binary Features

2001 ◽  
pp. 305-329 ◽  
Author(s):  
Jing Zhang ◽  
Shunichiro Oe
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Texture Image ◽  
Segmentation Method ◽  
Texture Image Segmentation ◽  
Binary Features

Research on Low Quality Texture Image Segmentation Background

2014 ◽  
Vol 687-691 ◽  
pp. 3616-3619
Author(s):  
Ning Liu ◽  
Hong Xia Wang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Experimental Results ◽  
Texture Image ◽  
Segmentation Method ◽  
Otsu Thresholding ◽  
Complex Background ◽  
Segmentation Methods ◽  
Texture Image Segmentation

In image processing, the texture image segmentation is one of the most important issues. Considering the problem that the traditional segmentation methods often fail to the low quality texture image segmentation, this paper proposes a modified OTSU thresholding segmentation method. Experimental results show that the proposed method not only is well adapt to the change of brightness and contrast, but also can be applied to much complex background.

Mean Shift Based Adaptive Texture Image Segmentation Method

2010 ◽  
Vol 21 (6) ◽  
pp. 1451-1461 ◽  
Author(s):  
Shuang WANG ◽  
Yu XIA ◽  
Li-Cheng JIAO
Keyword(s):  
Image Segmentation ◽  
Mean Shift ◽  
Texture Image ◽  
Segmentation Method ◽  
Texture Image Segmentation

Texture Image Segmentation Based on MRMRF in Contourlet Domain

2012 ◽  
Vol 532-533 ◽  
pp. 732-737
Author(s):  
Xi Jie Wang ◽  
Xiao Fan Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Field Model ◽  
Model Parameters ◽  
Texture Image ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Texture Image Segmentation

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

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