ON THE ACCURACY OF SELECTED IMAGE TEXTURE SEGMENTATION METHODS

Tongue Image Texture Segmentation Based on Gabor Filter Plus Normalized Cut

Lecture Notes in Computer Science - Medical Biometrics ◽

10.1007/978-3-642-13923-9_12 ◽

2010 ◽

pp. 115-125 ◽

Cited By ~ 1

Author(s):

Jianfeng Li ◽

Jinhuan Shi ◽

Hongzhi Zhang ◽

Yanlai Li ◽

Naimin Li ◽

...

Keyword(s):

Gabor Filter ◽

Texture Segmentation ◽

Image Texture ◽

Normalized Cut

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Texture Segmentation of Jacquard Fabric Using Wavelet-Domain Markov Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.468-471.2720 ◽

2012 ◽

Vol 468-471 ◽

pp. 2720-2723

Author(s):

Yang Zhang ◽

You Cheng Tong ◽

Jun Zhou Yao

Keyword(s):

Markov Model ◽

Low Frequency ◽

Gaussian Mixture ◽

Texture Segmentation ◽

Image Texture ◽

Distribution Model ◽

Wavelet Domain ◽

Markov Random ◽

Multi Level ◽

Fabric Design

To improve the accuracy and efficiency of fabric design CAD, a wavelet-domain Markov model to image texture segmentation from a natural framework for intergrating both local and global information of jacquard fabric image behavior, together with contextual information.Firstly the Daubechies wavelet and tree-structure is selected, then the approach decomposes the low frequency part of the jacquard fabric image. Secondly within the theoretical framework of Markov random field, we construct the grey field distribution model and label field prior model with finite Gaussian mixture algorithm and multi-level logistic algorithm respectively. The experiments for almost 30 warp knitting jacquard fabric images show that this approach is a feasible way for jacquard fabric, and it supplies a theoretical platform for subsequent research.

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WAVELET-BASED SALIENT ENERGY POINTS FOR UNSUPERVISED TEXTURE SEGMENTATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001405004113 ◽

2005 ◽

Vol 19 (03) ◽

pp. 429-458 ◽

Cited By ~ 2

Author(s):

M. K. BASHAR ◽

N. OHNISHI

Keyword(s):

Texture Features ◽

Texture Segmentation ◽

Image Texture ◽

Discrete Wavelet ◽

Salient Point ◽

Unsupervised Segmentation ◽

Image Texture Analysis ◽

Point Distribution ◽

Binary Images ◽

Time Systems

Despite extensive research on image texture analysis, it is still problematic to characterize and segment texture images especially in the presence of complex patterns. Upon tremendous advancement of the internet and the digital technology, there is also a need for the development of simple but efficient algorithms, which can be adaptable to real-time systems. In this study, we propose such an approach based on multiresolution discrete wavelet transform (DWT). After the transform, we compute salient energy points from each directional sub-band (LH, HL, and HH) in the form of binary image by thresholding intermittency indices of wavelet coefficients. We then propose and extract two new texture features namely Salient Point Density (SPD) and Salient Point Distribution Nonuniformity (SPDN) based on the number and the distribution of salient pixels in the local neighborhood of every pixel of the multiscale binary images. We thus obtain a set of feature images, which are subsequently applied to the popular K-means algorithm for the unsupervised segmentation of texture images. Though the above representation appear simple and infrequent in the literature, it proves useful in the context of texture segmentation. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potentiality of the proposed approach.

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