Fast Texture Segmentation Based on Semi-local Region Descriptor and Active Contour Driven by the Bhattacharyya Distance

2010 ◽  
Author(s):  
Shanqing Zhang ◽  
Weibin Xin ◽  
Guixu Zhang
Keyword(s):  
Active Contour ◽  
Texture Segmentation ◽  
Local Region ◽  
Bhattacharyya Distance ◽  
Region Descriptor

An Improved Image Segmentation Active Contour Model

2014 ◽  
Vol 513-517 ◽  
pp. 3463-3467
Author(s):  
Li Fen Zhou ◽  
Chang Xu Cai
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Local Region ◽  
Intensity Inhomogeneity ◽  
Contour Model ◽  
Global And Local ◽  
Region Information

The Chan-Vese (C-V) active contour model has low computational complexity, initialization and insensitive to noise advantagesand utilizes global region information of images, so it is difficult to handle images with intensity inhomogeneity. The Local binary fitting (LBF) model based on local region information has its certain advantages in mages segmentation of weak boundary or uneven greay.but , the segmentation results are very sensitive to the initial contours, In order to address this problem, this paper proposes a new active contour model with a partial differential equation, which integrates both global and local region information. Experimental results show that it has a distinctive advantage over C-V model for images with intensity inhomogeneity, and it is more efficient than LBF.

Active Contour Model with Edge Flow Vector for Texture Segmentation

2015 ◽  
Vol 781 ◽  
pp. 511-514
Author(s):  
Tanunchai Boonnuk ◽  
Sanun Srisuk ◽  
Thanwa Sripramong
Keyword(s):  
Medical Imaging ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Segmentation ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Texture Image ◽  
Contour Model ◽  
Flow Vector ◽  
Edge Flow

In this paper, we propose effective method for texture segmentation using active contour model with edge flow vector. This technique was applied from previous active contour model that uses gradient vector flow as external force. It was observed that our method provided better results for texture segmentation while a traditional active contour model and active contour model with gradient vector flow were not capable to be used with texture image. Thus, texture image such as medical imaging can be identified using active contour model with edge flow vector.

Active Contour Model Coupling Local and Global Information

2015 ◽  
Vol 719-720 ◽  
pp. 1155-1159 ◽  
Author(s):  
Zhi Feng Wang
Keyword(s):  
Model Experiment ◽  
Active Contour ◽  
Active Contour Model ◽  
Data Fitting ◽  
Local Region ◽  
Model Coupling ◽  
Global Information ◽  
Contour Model ◽  
Intensity Information ◽  
Cv Model

The local region term and global region term are combined for image segmentation. Intensity information in local regions is utilized by adding a kernel function in the data fitting term. Experiments have been done on different images to compare the effectiveness of our methods with that of the classic CV model, Li’s Local Binary Fitting (LBF) model. Experiment results show that the new model maintain more satisfactory segmentation results.

Texture Segmentation of Natural Images Based on Active Contour Model

2012 ◽  
Vol 546-547 ◽  
pp. 553-558
Author(s):  
Zhan Wang ◽  
Yun Hui Yan ◽  
De Wei Dong ◽  
Ke Chen Song
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Synthesis ◽  
Texture Features ◽  
Texture Segmentation ◽  
Natural Images ◽  
Contour Model ◽  
Signal Features ◽  
Environmental Image

To segment complex texture natural environment images; the first, the texture features of natural images should be analysed and the texture features should be extracted; The second, texture images segmengtation can be achieved by using Mumford-Shah active contour model, this segmentation model can better process fuzzy, default boundary, and this model can be solved by level set method. This method can express well complex texture signal features of natural images. Through making texture segmentation experiment for standard texture synthesis image and natural environmental image, its results show that the texture segmentation based on Mumford-Shah active contour model can segment natural images.

A novel local region-based active contour model for image segmentation using Bayes theorem

2020 ◽  
Vol 506 ◽  
pp. 443-456 ◽  
Author(s):  
Yupeng Li ◽  
Guo Cao ◽  
Tao Wang ◽  
Qiongjie Cui ◽  
Bisheng Wang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Bayes Theorem ◽  
Local Region ◽  
Contour Model
Sign in / Sign up

Export Citation Format

Share Document