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Review of wavelet‐based unsupervised texture segmentation, advantage of adaptive wavelets

  • Published:2018-09 Issue:9 Volume:12 Page:1626-1638
  • ISSN:1751-9667
  • Container-title:IET Image Processing
  • language:en
  • Short-container-title:IET Image Processing

Author:

Huang Yuan1,De Bortoli Valentin2,Zhou Fugen1,Gilles Jérôme3ORCID

Affiliation:

1. Image Processing Center, Beihang UniversityBeijing100191People's Republic of China

2. Centre pour les Mathématiques et Leurs ApplicationsEcole Normale Supérieure of Paris Saclay61 Avenue du Président Wilson94230CachanFrance

3. Department of Mathematics and StatisticsSan Diego State University5500 Campanile DriveSan DiegoCA92182USA

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference70 articles.

1. Statistical and structural approaches to texture;Hawlick R.M.;Proc. IEEE,1979

2. 2013 MIRAGE'13 Conf. Berlin Germany A. Rampun H. Strange R. Zwiggelaar Texture segmentation using different orientations of GLCM features

3. Wavelet-based texture analysis and synthesis using hidden markov models

4. Unsupervised texture segmentation using Markov random field models;Manjunath B.S.;IEEE Trans. Pattern Anal. Mach. Intell.,1991

5. Spatial Interaction and the Statistical Analysis of Lattice Systems

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1. Unsupervised Texture Image Segmentation Based on Local Features;2023 Seminar on Information Computing and Processing (ICP);2023-11-27

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3. Bio-Medical Image Segmentation using Wavelet Based Fusion Technique;Biomedical and Pharmacology Journal;2022-06-30

4. A partial image encryption scheme based on DWT and texture segmentation;Cogent Engineering;2022-02-07

5. A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities;IEEE Access;2022
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