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研究生:郭鴻志
研究生(外文):Tempest Guo
論文名稱:影像分割中以小波轉換為基礎的紋理分析
論文名稱(外文):Wavelet-based Texture Analysis for Image Segmentation
指導教授:林信鋒林信鋒引用關係
指導教授(外文):David Shin-feng Lin
學位類別:碩士
校院名稱:國立東華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:英文
論文頁數:50
中文關鍵詞:紋理分析紋理分割
外文關鍵詞:texture analysistexture segmentation
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摘 要
由於紋理具有強烈的直覺性,使得要用數學模型描述紋理並不是一件容易的事。因此以紋理為基礎的影像分割一直是個深具挑戰性的問題。大部分的紋理模型採用空間上或頻域上區域性的統計量做為特徵;此外,也有一些以結構為基礎的模型則是藉著基本元素和元素的排放規則來描述紋理。
在本篇論文中提出了一種新的紋理特徵,它是以Gabor小波為基礎的所發展出來的。在計算時,這種特徵利用不同的波長和角度的組合來計算影像中每個點的特徵值。實驗的結果顯示了這種新的紋理特徵在紋理分類時的效率勝過其他的特徵;以紋理為基礎的影像分割,以及在不同參數下的Gabor小波也包含在實驗結果中。

Abstract
Texture segmentation is a challenging problem because the strong intuitive property of visual texture makes it is difficult to be modeled. Most traditional algorithms measure the local statistical property in spatial domain or frequency domain, some other structural-based approaches make description of the primitives of texture and its placement rule.
A new texture feature based on Gabor wavelet is proposed in this thesis. In the proposed method, texture feature is calculated for each pixel with two parametersλ(wavelength) andψ(orientation). Experimental result shows that the proposed textural feature is more effective than others in texture classification. The segmentation result and some simulation results are reported.

Content
Chapter 1 INTRODUCTION
1.1 Overview
1.2 Motivation
1.3 Organization of this Thesis
Chapter 2 TEXTURE AND SEGMENTATION
2.1 Image Segmentation Techniques
2.1.1 Histogram-based Techniques
2.1.2 Edge-based Techniques
2.1.3 Region-based Techniques
2.1.4 Markov Random Field-based Techniques
2.1.5 Hybrid Techniques
2.1.6 Other approaches
2.2 Analysis and Representation of Texture
2.2.1 Autocorrelation Functions
2.2.2 Primitive Length
2.2.3 Gray Level Co-occurrence Matrix (GLCM)
2.2.4 Markov Random Field
2.2.5 Fourier Analysis
2.2.6 Low’s Energy
2.2.7 Edge Frequency
2.2.8 Fractal Dimension
2.3 Texture Segmentation
Chapter 3 WAVELET-BASED TEXTURE SEGMENTATION
3.1 The Perception of Texture and Gabor Wavelets
3.2 Gabor Wavelet-based Texture Feature
Chapter 4 PERFORMANCE EVALUATION
4.1 Performance Measure
4.2 Experimental Result
Chapter 5 CONCLUSION
5.1 Summary
5.2 Applications of Texture Segmentation
5.3 Future Works
Bibliography

[Books]
[1] S.I. Landau, Webster Illustrated Contemporary Dictionary, Doubleday, 1984
[2] IEEE Standard 610.4-1990, IEEE Standard Glossary of Image Processing and Pattern Recognition Terminology, IEEE Press, 1990
[3] P. Brodatz: Textures, a photographic album for artists and designers, Dover Publications, New York, 1966
[4] John C. Russ, The Image Processing Handbook, 2nd Ed, CRC Press, 1995
[5] Kenneth R. Castleman, Digital Image Processing, Prentice Hall, 1996
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[7] Rafael C Gonzalez and Richard E Woods, Digital Image Processing, Addison Wesley, 1993
[8] A Murat Tekalp, Digital Video Processing, Prentice Hall, 1995
[9] Weidong Kou, Kluwer, Digital Image Compression: algorithms and standards, Academic Publishers, 1995
[10] Jean-Nichel Morel and Sergio Solimini, Variational Methods in Image Segmentation, Birkhauser, 1995
[11] Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989
[12] Ingrid Daubechies, Ten Lecture on Wavelets, Society for Industrial and Applied Mathematics, 1992
[13] J. R. Parker, Algorithms for Image Processing and Computer Vision, Wiley Computer Publishing, 1997
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[15] Brain D. Ripley, Spatial Statistics, John Wiley & Sons, 1981
[Journal Papers]
[16] Dennis Dunn et al, Texture Segmentation Using 2-D Gabor Elementary Functions, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.16 No2, 1994
[17] Andrew Laine and Jian Fan, An Adaptive Approach for Texture Segmentation by Multi-channel Wavelet Frames, Center for Computer Vision and Visualization Technical Report, TR-93-025, 1993
[18] J. Zhang et al,A Wavelet-based Multiresolution Statistical Model for Texture, IEEE Trans. on Image Processing, Vol.7 No.11, 1998
[19] G. Van de Wouwer et al, Statistical Texture Characterization from Discrete Wavelet Representation, IEEE Trans. on Image Processing, Vol.8 No.4, 1999
[20] Mary L. Comer et al, Segmentation of Textured Image Using a Multiresolution Gaussian Autoregressive Model, IEEE Trans. on Image Processing, Vol.8 No.3, 1999
[21] Trygve Randen and John Hakon Husoy, Texture Segmentation Using Filters with Optimized Energy Separation, IEEE Trans. on Image Processing, Vol.8 No.4, 1999
[22] Philippe Andrey and Philippe Tarroux, Unsupervised Segmentation of Markov Random Field Modeled Textured Images Using Selectionist Relaxation, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.20 No.3, 1998
[23] Thomas Hofmann et al, Unsupervised Texture Segmentation in a Deterministic Annealing Framework, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.20 No.8, 1998
[24] George M. Haley and B. S. Manjunath, Rotation-Invariant Texture Classification Using a Complete Space-Frequency Model, IEEE Trans. on Image Processing, Vol.8 No.2, 1999
[25] Stefan Pittner and Sagar V. Kamarthi, Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.21 No.1, 1999
[26] Peter Schroder and Wim Sweldens, Spherical Wavelets: Texture Processing
[27] Vassili A. Kovalev et al, Texture Anisotropy in 3-D images, IEEE Trans. on Image Processing, Vol.8 No.3, 1999
[28] Xiaoou Tang, Texture Information in Run-length Matrices, IEEE Trans. on Image Processing, Vol.7 No.11, 1998
[29] Giovanni Jacovitti et al, Texture Synthesis-by-Analysis with Hard-Limited Gaussian Process, IEEE Trans. on Image Processing, Vol.7 No.11, 1998
[30] A. Hojjatoleslami and J. Kittler, Region Growing: A New Approach, IEEE Trans. on Image Processing, Vol.7 No.7, 1998
[31] Kostas Haris et al, Hybrid Image Segmentation Using Watersheds and Fast Region Merging, IEEE Trans. on Image Processing, Vol.7 No.12, 1998
[32] Iraj Sodagar et al, Scalable Wavelet Coding for Synthetic/Natural Hybrid Images, IEEE Trans. on Circuits and Systems for Video Technology, Vol.9 No.2, 1999
[33] P. Besl and R. Jain, Segmentation through variable-order surface fitting, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol.10 1998
[34] T. Pavlidis and Y. Liow, Integrating region growing and edge detection, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol.12 1998
[35] L.D Griffin et al, Scale and segmentation of grey-level image using maximum gradient paths, Proceedings Image and Visual Computing, 1992
[36] R. Chellappa and S. Chatterjee, Classification of Textures Using Gaussian Markov Random Fields, IEEE Trans. on Acoustics Speech and Signal Processing, 1985
[Conference Papers]
[37] Andrea Marazzi et al, Automatic Selection of the number of Clusters in Multidimensional Data Problems, Proceedings ICIP-96, VOL.III, 1996
[38] Maria-Elena et al, Unsupervised Segmentation Based On Robust Estimation and Co-occurrence Data, Proceedings ICIP-96, Vol. III, 1996
[39] Mary L. Comer et al, The EM/MPM Algorithm for Segmentation of Textured Images: analysis and further experimental results, Proceedings ICIP-96, Vol. III, 1996
[40] Thomas P. Weldon et al, Integrated Approach to Texture Segmentation Using multiple Gabor Filters, Proceedings ICIP-96, Vol. III, 1996
[41] V. Kumar et al, Unsupervised Model-based Object Recognition by Parameter Estimation of Hierarchical Mixtures, Proceedings ICIP-96, Vol. III, 1996
[42] Dhiraj Kracker et al, New Subband Geometries for Image Texture Segmentation, Proceedings ICIP-96, Vol. III, 1996
[43] Christophe Collet et al, Hierarchical MRF Modeling for Sonar Picture Segmentation, Proceedings ICIP-96, Vol. III, 1996
[44] Jesse Bennett et al, Multispectral and Color Image and Synthesis using Random Field Models, Proceedings ICIP-96, Vol. III, 1996
[45] Eero P. Simoncelli and Javier Portilla, Texture Characterization via Joint Statistics of Wavelet Coefficient Magnitudes, Proceedings ICIP-98, Vol. I, 1998
[46] Li-Chang Liu et al, Texture Segmentation Using Overcomplete-wavelet-frame based Fractal Signatures, Proceedings ICSW-IPCR, 1998
[47] Tao-I Hsu et al, Texture Segmentation based on the Fractal Analysis, Proceedings ICSW-IPCR, 1998
[48] Chien-Chang Chen and Chaur-Chin Chen, Zerotree Wavelet Transform for Texture Discrimination, Proceedings CVGIP, 1998
[49] A.Betti et al, Using a Wavelet-based Fractal Feature to Improve Texture Discrimination on SAR Images, Proceedings ICIP-97, Vol. II, 1997
[50] A.Teuner et al, Orientation- and Scale-Invariant Recognition of Textures in Multi-object Scenes, Proceedings ICIP-97, Vol. II, 1997
[51] J.You et al, Fractional Discrimination for Texture Image Segmentation, Proceedings ICIP-97, Vol. II, 1997
[52] Marcia G. Ramos et al, Psychovisually-based Multiresolution Image Segmentation, Proceedings ICIP-97, Vol. II, 1997
[53] John R. Smith and Shih-Fu Chang, Quad-tree Segmentation for Texture-based Image Query, Proceedings of 2nd Annual ACM Multimedia Conference, 1994
[54] Haralick et al, Textural Features for Image Classification, IEEE Trans. on Systems Man and Cybernetics, Nov 1973.
[55] R.W. Conners and C.A. Harlow, A Theoretical Comparison of Texture Algorithms, IEEE Trans. on Pattern Analysis and Machine Intelligence, 1980
[56] J.S. Weszka et al, A Comparative Study of Texture Measures for Terrain Classification, IEEE Trans. on Systems Man and Cybernetics, 1976
[57] R.W. Conners et al, Segmentation of a High-Resolution Urban Scene using Texture Operators, Computer Vision, Graphics and Image Processing, 1984
[Internet Resources]
[58] USC Signal and Image Processing Institute (Brodatz texture image database)
http://sipi.usc.edu/services/database/Database.html
[59] VisTex texture image database (Vision and Modeling group, MIT Media Lab)
http://www-white.media.mit.edu/vismod/imagery/VisionTexture/vistex.html
[60] Texture Representation (J.R Smith's Ph.D. thesis)
http://disney.ctr.columbia.edu/jrsthesis/node41.html
[61] Texture Segmentation -- An Introductory Primer
http://robotics.stanford.edu/~ruzon/tex_seg/intro_to_tex_seg.html
[62] Autoregressive Texture Segmentation and Synthesis for Wavelet Image Compression
http://www.cs.sc.edu/~kubota/wavelet/texture.html
[63] MeasTex (Measurement of Texture)
http://www.cssip.elec.uq.edu.au/~guy/meastex/meastex.html
[64] Segmentation of Textured Files
http://www.cs.unibonn.de/image/segmentation.html
[65] CMU The Computer Vision Homepage
http://www.cs.cmu.edu/~cli/vision.html
[66] Topics in Computer Vision: The analysis of visual texture
http://www.stanford.edu/class/cs328b/
[67] Machine Vision Information Library
http://www.vision1.com/library.html
[68] Wavelets for Texture Analysis
http://zeus.ruca.ua.ac.be/VisionLab/wta/wta.html
[69] Segmentation
http://www.neuroinformatik.de/ini/VDM/research/computerVision/segmentation/contents.html
[70] Gabor Wavelets
http://www.neuroinformatik.ruhrunibochum.de/ini/VDM/research/computerVision/imageProcessing/wavelets/gabor/contents.html
[71] An Introduction to Wavelets: The Neural Perspective
http://www.fen.bris.ac.uk/engmaths/research/slide_show/slide_show.html
[72] Classification, Segmentation and Automated Visual Inspection
http://www.brunel.ac.uk/depts/ee/Research_Programme/NN/pubhome.html
[73] Texture Segmentation on MARS
http://robotics.stanford.edu/~ruzon/NASA/texture.html

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