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Student Number 90522030
Author antony kuo(郭正德)
Author's Email Address No Public.
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Department Computer Science and Information Engineering
Year 2002
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title Applications of Wavelet Transforms on Textured Images:Defect Inspection and Synthesis
Date of Defense 2003-06-19
Page Count 56
Keyword
  • Defect Inspection
  • Synthesis
  • texture
  • wavelet transform
  • Abstract Due to the emerging of computer technologies, the functions and quality of imaging devices, such as digital camera, digital camcorder, and scanner, have been continuously improved. Moreover, the cost of these devices is also rapidly reduced. The content conveyed by multimedia is thus more splendid and richer. The proper management of the image data is thereby more and more important. The features that describe image data are mainly represented by using color, shape, and texture. In this thesis, we will elaborate on the analysis of texture and its application in image analysis.
    The main purpose of this dissertation is to adopt the concept of wavelet transform and apply it to defect detection and texture synthesis in texture images. In texture defect detection, the defects can be discriminated according to the distribution ranges of wavelet coefficients between the normal and defective parts of texture images. In traditional texture defect detection methods, the normal parts of texture images have to be trained in advance. In this thesis, we propose a novel method to automatically determine the training regions based on the characteristics exhibited by normal and defective texture images. In this way, the detection error can be reduced because of the avoiding of environmental changes.
    In texture synthesis, texture edge features can be extracted according to the characteristics of wavelet transformation, that is different frequency bands will exhibit different information. By combining horizontal and vertical edge information, the basic blocks of textures can be built. Original images can be synthesized by the extracted basic blocks. Moreover, we utilize the proposed texture defect detection method to verify the synthesis results.
    Table of Content 目錄
    Abstract..............................................I
    摘要................................................III
    目錄.................................................IV
    附圖目錄.............................................VI
    第一章 緒論...........................................1
    1.1 研究動機 .........................................1
    1.2 相關研究..........................................3
    1.3 論文架構..........................................5
    第二章 小波理論.......................................6
    2.1小波轉換的演進.....................................7
    2.2 基底函數..........................................9
    2.2.1 小波函數.......................................10
    2.2.2 尺度函數.......................................11
    2.2.3 基底函數特性...................................12
    2.3 小波轉換.........................................13
    2.3.1 連續小波轉換與離散小波轉換.....................14
    2.3.2 一維小波轉換與二維小波轉換.....................15
    2.4 應用於影像的小波轉換.............................18
    第三章 紋理瑕疵檢測..................................21
    3.1 方法簡介.........................................22
    3.2 特徵抽取.........................................24
    3.3 基底函數與小波階數選擇...........................26
    3.3.1 基底函數選擇...................................26
    3.3.2 小波階數選擇...................................27
    3.4 訓練樣本取樣.....................................29
    3.4.1 人工取樣.......................................29
    3.4.2 自動取樣.......................................30
    第四章 紋理合成......................................32
    4.1 方法簡介.........................................33
    4.2 邊緣資訊抽取.....................................35
    4.3 重覆規則尋找.....................................37
    4.4 紋理合成與檢測...................................38
    第五章 實驗結果與討論................................40
    5.1 紋理瑕疵檢測實驗結果.............................41
    5.2 紋理瑕疵檢測實驗討論.............................45
    5.3 紋理合成實驗結果.................................47
    5.4 紋理合成實驗討論.................................51
    第六章 結論與未來工作................................52
    6.1 結論.............................................52
    6.2 未來工作.........................................53
    參考文獻.............................................54
    Reference [1] A.L. Amet, A. Ertuzun, and A. Ercil, “Texture defect detection using subband domain co-occurrence matrices,” Image Analysis and Interpretation, pp. 205-210 Apr 1998.
    [2] A. Kumar and G. Pang, “Identification of surface defects in textured  materials using wavelet packets,” Industry Applications Conference, Thirty-Sixth IAS Annual Meeting. Conference Record of the 2001 IEEE , Volume: 1 , pp.247 -251 30 Sep-4 Oct 2001.
    [3] C.A. Bouman and B. Liu, “Multiple Resolution Segmentation of Textured Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 2, pp. 99-113, Feb. 1991.
    [4] C. Neubauer , “Segmentation of defects in textile fabric,” IEEE Pattern Recognition,Vol.1.Conference A:Computer Vision and Applications , pp688-691,Aug. 1992.
    [5] C. A. Bouman and M. Shapiro, “A multiscale random field model for Bayesian image segmentation,” IEEE Trans. Image Processing, vol. 3, pp. 162–177, Mar. 1994.
    [6] C.S. Lu, P.C. Chung, and C.F. Chen, “Unsupervised Texture Segmentation via Wavelet Transform,” Pattern Recognition, vol. 30, no. 5, pp. 729-742, 1997.
    [7] C.T. Li, “Unsupervised Image Segmentation Using Multiresolution Markov Random Fields,” PhD thesis, Univ. of Warwick, U.K., 1998.
    [8] E. Salari and Z. Ling, “Texture segmentation using hierarchical wavelet decomposition, ” Industrial Electronics, Proceedings of the IEEE International Symposium on 10-14 Jul 1995.
    [9] G. Lohmann , “Co-occurrence-based analysis and synthesis of textures,”Pattern Recognition, Vol. 1 - Conference A: Computer Vision & Image Processing. pp.449-453 9-13 Oct 1994.
    [10] H. Choi, J. K. Romberg, R. G. Baraniuk, and N. G. Kingsbury, “Hidden Markov tree modeling of complex wavelet transforms,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, Istanbul, Turkey, June 2000.
    [11] H. Choi and R. Baraniuk, “Multiscale image segmentation using wavelet-domain hidden Markov models,” IEEE Trans. Image Processing,vol. 10, pp. 1309–1321, Sept. 2001.
    [12] H. Fujiwara, Z. Zhong , and K. Hashimoto , “Toward automated inspection of textile surfaces: removing the textural information by using wavelet shrinkage,” IEEE International Conference on , Volume: 4 , pp. 3529 –3534,2001.
    [13] M. Unser, “Texture classification and segmentation using wavelet frames,” IEEE Trans. Image Processing, vol. 4, pp. 1549–1560, Nov.1995.
    [14] M. S. Crouse and R. G. Baraniuk, “Contextual hidden Markov models for wavelet-domain signal processing,” in Proc. 31th Asilomar Conf. M. L. Comer and E. J. Delp, “Segmentation of textured images using a multiresolution Gaussian autoregressive model,” IEEE Trans. Image Processing, vol. 8, pp. 408–420, Mar. 1999.
    [15] M. C. Lee and C.M. Pun,“Texture classification using dominant wavelet packet energy features,” Image Analysis and Interpretation, Proceedings. 4th IEEE Southwest Symposium , pp.301 -304 2000.
    [16] P. w. Chen , L. T. Chun ,and H. F. Yauetc , “Classifying textile faults with a back-propagation neural network using power spectra,” Textile Res.j.68(2),pp121-126,1998.
    [17] R. Ghozi, and B.C. Levy, “Phase transitions and multi-scale Markov random fields: application to texture synthesis,”Signals, Systems and Computers, pp. 1-5 1993.
    [18] R. Paget and I. D. Longstaff, “Texture synthesis via a noncausal nonparametric Markov random field,” IEEE Trans. Image Processing, vol.7, pp. 925–931, June 1998.
    [19] R. Wilson, A. Calway, and E.R.S. Pearson, “A Generalised Wavelet Transform for Fourier Analysis: The Multiresolution Fourier Transform and Its Application to Image and Audio Signal Analysis,” IEEE Trans. Information Theory, vol. 38, no. 2, Mar. 1992.
    [20] T. Chang and C.-C.J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Processing, vol.2 Issue: 4 , pp.429 –441 Oct 1993.
    [21] T. Chang, and C.-C.J. Kuo, “Texture segmentation with tree-structured wavelet transform,” ime-Frequency and Time-Scale Analysis, Proceedings of the IEEE-SP International Symposium , pp. 543 –546 4-6 Oct 1992.
    [22] T.-I. Hsu and R. Wilson, “A two-component model of texture for analysis and synthesis,” IEEE Trans. Image Processing, vol. 7, pp.1466–1476, Oct. 1998.
    [23] Z. Kato, M. Berthod, and J. Zerubia, “A Hierarchical Markov Random Field Model and Multi-Temperature Annealing forParallel Image Classification,” Technical Report 1938, INRIA, 1993.
    Advisor
  • Kuo-Chin Fan(范國清)
  • Files
  • 90522030.pdf
  • approve immediately
    Date of Submission 2003-07-01

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