Texture Segmentation Using SOM and Multi-scale Bayesian Estimation

2006 ◽  
pp. 661-668
Author(s):  
Tae Hyung Kim ◽  
Il Kyu Eom ◽  
Yoo Shin Kim
Keyword(s):  
Bayesian Estimation ◽  
Texture Segmentation ◽  
Multi Scale

Texture Segmentation Using Neural Networks and Multi-scale Wavelet Features

2005 ◽  
pp. 400-409 ◽  
Author(s):  
Tae Hyung Kim ◽  
Il Kyu Eom ◽  
Yoo Shin Kim
Keyword(s):  
Neural Networks ◽  
Texture Segmentation ◽  
Multi Scale ◽  
Wavelet Features

Statistical multi-scale laws’ texture energy for texture segmentation

2018 ◽  
Author(s):  
Mega Kusuma Wardhani ◽  
Xingru Yu ◽  
Jinping Li
Keyword(s):  
Texture Segmentation ◽  
Multi Scale

scholarly journals Unsupervised color texture segmentation based on multi-scale region-level Markov random field models

2019 ◽  
Vol 43 (2) ◽  
pp. 264-269
Author(s):  
X. Song ◽  
L. Wu ◽  
G. Liu
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Texture Segmentation ◽  
Unsupervised Segmentation ◽  
Spatial Constraints ◽  
Multi Scale ◽  
Markov Random ◽  
Single Scale ◽  
Proposed Model ◽  
Color Texture

In the field of color texture segmentation, region-level Markov random field model (RMRF) has become a focal problem because of its efficiency in modeling the large-range spatial constraints. However, the RMRF defined on a single scale cannot describe the un-stationary essence of the image, which highly limits its robustness. Hence, by combining wavelet transformation and the RMRF model, we present a multi-scale RMRF (MsRMRF) model in wavelet domainin this paper. In the Bayesian framework, the proposed model seamlessly integrates the multi-scale information stemmed from both the original image and the region-level spatial constraints. Therefore, the new model can accurately describe the characteristics of different kinds of texture. Based on MsRMRF, an unsupervised segmentation algorithm is designed for segmenting color texture images. Both synthetic color texture images and remote sensing images are employed in the comparative experiments, and the experimental results show that the proposed method can obtain more accurate segmentation results than the competitors.

A multi-scale texture segmentation method

2014 ◽  
Author(s):  
Jian-nong Cao ◽  
Ping-lu Wang ◽  
Yu-wei Dong ◽  
Qi-gao Xu
Keyword(s):  
Texture Segmentation ◽  
Segmentation Method ◽  
Multi Scale

Texture segmentation based on nonlinear compact multi-scale structure tensor and TV-flow

2016 ◽  
Author(s):  
Wei Xu ◽  
Shou-Dong Han ◽  
Yu-Chen Peng
Keyword(s):  
Texture Segmentation ◽  
Scale Structure ◽  
Structure Tensor ◽  
Multi Scale ◽  
Tv Flow
Sign in / Sign up

Export Citation Format

Share Document