CTex—An Adaptive Unsupervised Segmentation Algorithm Based on Color-Texture Coherence

2008 ◽  
Vol 17 (10) ◽  
pp. 1926-1939 ◽  
Author(s):  
D.E. Ilea ◽  
P.F. Whelan
Keyword(s):  
Segmentation Algorithm ◽  
Unsupervised Segmentation ◽  
Color Texture

Unsupervised segmentation of wear particle’s image using local texture feature

2018 ◽  
Vol 70 (9) ◽  
pp. 1601-1607 ◽  
Author(s):  
Hong Liu ◽  
Haijun Wei ◽  
Haibo Xie ◽  
Lidui Wei ◽  
Jingming Li
Keyword(s):  
Texture Feature ◽  
Segmentation Algorithm ◽  
Great Promise ◽  
Identification System ◽  
Wear Particles ◽  
Small Particles ◽  
Unsupervised Segmentation ◽  
Edge Image ◽  
Content Type ◽  
Local Texture

Purpose The possibility of using a pattern recognition system for wear particle analysis without the need of a human expert holds great promise in the condition monitoring industry. Auto-segmentation of their images is a key to effective on-line monitoring system. Therefore, an unsupervised segmentation algorithm is required. The purpose of this paper is to present a novel approach based on a local color-texture feature. An algorithm is specially designed for segmentation of wear particles’ thin section images. Design/methodology/approach The wear particles were generated by three kinds of tribo-tests. Pin-on-disk test and pin-on-plate test were done to generate sliding wear particles, including severe sliding ones; four-ball test was done to generate fatigue particles. Then an algorithm base on local texture property is raised, it includes two steps, first, color quantization reduces the total quantity of the colors without missing too much of the detail; second, edge image is calculated and by using a region grow technique, the image can be divided into different regions. Parameters are tested, and a criterion is designed to judge the performances. Findings Parameters have been tested; the scale chosen has significant influence on edge image calculation and seeds generation. Different size of windows should be applied to varies particles. Compared with traditional thresholding method along with edge detector, the proposed algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the present method is suited for wear particles’ image segmentation and can be put into practical use in wear particles’ identification system. Research limitations/implications One major problem is when small particles with similar texture are attached, the algorithm will not take them as two but as one big particle. The other problem is when dealing with thin particles, mainly abrasive particles, the algorithm usually takes it as a single line instead of an area. These problems might be solved by introducing a smaller scale of 9 × 9 window or by making use of some edge enhance technique. In this way, the subtle edges between small particles or thin particles might be detected. But the effectiveness of a scale this small shall be tested. One can also magnify the original picture to double or even triple its size, but it will dramatically increase the calculating time. Originality/value A new unsupervised segmentation algorithm is proposed. Using the property of the edge image, we can get target out of its background, automatically. A rather complete research is done. The method is not only introduced but also completely tested. The authors examined parameters and found the best set of parameters for different kinds of wear particles. To ensure that the proposed method can work on images under different condition, three kinds of tribology tests have been carried out to simulate different wears. A criterion is designed so that the performances can be compared quantitatively which is quite valuable.

scholarly journals Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)

2013 ◽  
Author(s):  
Qaiser Mahmood ◽  
Mohammad Alipoor ◽  
Artur Chodorowski ◽  
Andrew Mehnert1 ◽  
Mikael Persson
Keyword(s):  
Mean Shift ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Brain Segmentation ◽  
Mr Images ◽  
Unsupervised Segmentation ◽  
Fully Automatic ◽  
The Brain ◽  
Mr Brain ◽  
Dice Index

In this paper, we validate our proposed segmentation algorithm called Bayesian-based adaptive mean-shift (BAMS) on real mul-timodal MR images provided by the MRBrainS challenge. BAMS is a fully automatic unsupervised segmentation algorithm. It is based on the adaptive mean shift wherein the adaptive bandwidth of the kernel for each feature point is estimated using our proposed Bayesian approach [1]. BAMS is designed to segment the brain into three tissues; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The performance of the algorithm is evaluated relative to the manual segmentation (ground truth). The results of our proposed algorithm show the average Dice index 0.8377±0.036 for the WM, 0.7637±0.038 for the GM and 0.6835 ±0.023 for the CSF.

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.

Sign in / Sign up

Export Citation Format

Share Document