跳到主要內容

臺灣博碩士論文加值系統

(18.97.9.172) 您好!臺灣時間:2025/02/12 02:50
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:蔡宗恆
研究生(外文):Tsung-Heng Tsai
論文名稱:生物啟發之色彩與質感混合邊界偵測模型
論文名稱(外文):Biologically-Inspired Model for Hybrid-Order Chromatic Texture Boundary Detection
指導教授:林進燈林進燈引用關係周志成周志成引用關係
指導教授(外文):Chin-Teng LinChi-Cheng Jou
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系所
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:2005
畢業學年度:93
語文別:英文
論文頁數:87
中文關鍵詞:色彩質感賈伯人類視覺系統
外文關鍵詞:chromatic textureGaborhuman visual system
相關次數:
  • 被引用被引用:0
  • 點閱點閱:201
  • 評分評分:
  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:2
本論文提出一個由生物觀點所啟發的多階次質感邊界偵測演算法。在演算法的發展階段,我們成功地整合了三個重要的視覺元素:明度、質感、色彩。現今相關研究的盲點在於僅從應用觀點出發,無法對整個議題做出完整的探討。有鑑於此,本論文對於人類視覺系統的基本運作模式乃至系統化的整合過程進行相關的基礎研究,針對質感邊界偵測的議題完成了通盤的探討。此外,在多階次的質感特徵萃取過程中,一些為過去研究所忽略但卻極其重要的議題,例如:偽反應(false response)的產生,權重值的選定等等,我們也做了徹底地討論並提出解決的方案。
人類視覺系統能夠有效率地處理視覺資訊的關鍵在於拮抗式的傳送機制,諸如接收域(receptive field)的組成方式以及對比色(opponent color)的形成。本論文參考視覺系統的編碼方式,並以系統化的方式建構出完整的質感邊界偵測演算法。輸入的彩色影像首先被解構為三組對比色軸,並經由高斯(Gaussian)濾波器以及賈伯(Gabor)濾波器萃取質感的一階特徵及二階特徵,輔以本文所提出之適應性權重值決定法則得到兩者對應邊界之權重值,我們可以結合出多階次的質感邊界。經由大量的測試結果,我們發現均勻質感之間的邊界都可以成功而精確地被標定,而對於較不規則或不均勻的質感圖形,演算法仍會找出一些符合我們人眼感受的特性。除了令人滿意的測試結果,本演算法的處理過程極為簡單且直觀,不需導入過多的假設以及任何的訓練過程。相較現有研究,本論文深具應用潛力。
In this thesis, a hybrid-order texture boundary detection technique inspired from human visual system (HVS) was presented. The proposed algorithm integrates three important visual primitives: luminance, texture, and color into a functional system. At present, the related works were developed for specific applications such that an overall investigation of the texture segregation process would be inaccessible. Therefore, the thesis focuses on relevant fundamental researches on HVS and systematic integration to investigate the task of texture boundary detection thoroughly. Moreover, some critical but ignored issues from the procedure of hybrid-order feature extraction, such as false response, weights selection, etc., were also discussed and solved in this thesis.
Transmission with antagonism such as receptive field profile and opponent color is the critical point that HVS can effectively process visual information. This thesis employs the encoding form in HVS with systematic integration to build up a complete algorithm for texture boundary detection. Color images are firstly decomposed into three opponent axes and the 1st- and 2nd- order features are extracted by a Gaussian filter and Gabor filters. With the proposed adaptive weights selecting mechanism, the hybrid-order boundary can be obtained. Among extensive tests, boundaries between uniform textures can be detected successfully and accurately. For textures that are non-uniform or non-regular, the results also reflect some meaningful properties which are consistent to human visual sensation. In addition to the satisfying testing results, processing employed in this algorithm is very simple and intuitive with only few assumptions and no training procedure involved. Compared with the present researches, the proposed algorithm has a good application potential.
CHINESE ABSTRACT ii
ABSTRACT iii
ACKNOWLEDGEMENT iv
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES x
NOTATION AND ABBREVIATIONS xi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Statements 2
1.3 Related Works 4
1.3.1 Texture Analysis 4
1.3.2 Theories of Texture Perception 6
1.3.3 Linear Filtering Theory 8
1.3.4 Chromatic Texture 12
1.4 Research Scope 13
1.5 Outline of the Thesis 14
Chapter 2 Knowledge about Human Visual System 15
2.1 Anatomical Structure of Human Visual System 15
2.1.1 The Visual Pathway 15
2.1.2 Receptive Fields in Visual Pathway 17
2.2 Linear Filtering Theory 20
2.3 Color Vision 22
2.4 Feature Integration Theory 25
Chapter 3 Modeling Strategy 27
3.1 Luminance and Texture Features Extraction 28
3.1.1 Luminance 29
3.1.2 Texture 30
3.2 Hybrid-Order Feature Extraction 37
3.2.1 Color Decomposition 37
3.2.2 Feature Extraction 38
3.3 Operations for the Second-Order Features 41
3.3.1 Full-Wave Rectification and Gaussian Smoothing 41
3.3.2 False Responses to Non-Texture Regions 45
3.3.3 Features Reduction 49
3.4 Hybrid-Order Boundary Detection 50
3.4.1 The First- and Second-Order Boundary Detection 50
3.4.2 Boundary Combination 51
3.4.3 Local Peak Detection 54
3.5 Summary 54
Chapter 4 Experimental Results & Discussions 55
4.1 Parameters Selection 55
4.2 Experimental Comparisons 56
4.2.1 Experiment Ⅰ: Effect of Multi-Band Gabor Filters 57
4.2.2 Experiment Ⅱ: Effect of Color Information 60
4.2.3 Experiment Ⅲ: Effect of Hybrid-Order Features 62
4.3 Collection of Testing Results by Hybrid-Order Boundary Detection 64
4.3.1 Fully Boundary Detection 65
4.3.2 Partially Boundary Detection 71
4.4 Error Estimation 74
Chapter 5 Conclusions & Future Works 79
References 81
[1] B. Julesz, E. N. Gilbert, L. A. Shepp, and H. L. Frisch, “Inability of humans to discriminate between visual textures that agree in 2nd-order statistics--revisited,” Perception, vol. 2, pp. 391-405, 1973.
[2] D. Marr, Vision. San Francisco, CA: W. H. Freeman, 1982.
[3] W. Richards, “Quantifying sensory channels: Generalizing colorimetry to orientation and texture, touch, and tones,” Sensory Processes, vol. 3, pp. 207-229, 1979.
[4] J. K. Hawkins, “Textural properties for pattern recognition,” in Picture Processing and Psychopictorics, B. Lipkin and A. Rosenfeld Eds., Academic Press, New York, 1969.
[5] H. Tamura, S. Mori, and Y. Yamawaki, “Texture features corresponding to visual perception,” IEEE Transactions on System, Man, and Cybernetics, vol. 8, pp. 460-473, 1978.
[6] J. Sklansky, “Image segmentation and feature extraction,” IEEE Transactions on System, Man, and Cybernetics, vol. 8, pp. 237-247, 1978.
[7] B. Julesz, “Visual pattern discrimination,” IRE Transactions on Information Theory, IT-8, pp. 84-92, 1962.
[8] J. Beck, “Perceptual grouping produced by changes in orientation and shape,” Science, vol. 154, pp. 538-540, 1966.
[9] B. Julesz, “Experiments on the visual perception of texture,” Scientific American, vol. 232, pp. 34-43, 1975.
[10] B. Julesz, “Visual texture discrimination using random-dot patterns: Comment,” Journal of the Optical Society of America, vol. 69, pp. 268-270, 1978.
[11] B. Julesz, “Textons, the elements of texture perception and their interactions,” Nature, vol. 290, pp. 91-97, 1981.
[12] J. Beck, “Texture segmentation,” in Organization and Representation in Perception, J. Beck Ed., Hillside, NJ: Erlbaum, 1982.
[13] J. Beck, A. Sutter, and R. Ivry, “Spatial frequency channels and perceptual grouping in texture perception,” Computer Vision Graphics Image Processing, vol. 37, pp. 299-325, 1987.
[14] D. Gabor, “Theory of communication,” Journal of the Institution of Electrical Engineers, vol. 93, pp. 429-457, 1946.
[15] D. Marr and E. Hildreth, “Theory of edge detection,” Proceedings of the Royal Society of London (B), pp. 187-217, 1980.
[16] J. D. Daugman, “Two dimensional spectral analysis of cortical receptive field profiles,” Vision Research, vol. 20, pp. 847-856, 1980.
[17] J. D. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” Journal of the Optical Society of America A, vol. 2, pp. 1160-1169, 1985.
[18] J. R. Bergen and E. H. Adelson, “Early vision and texture perception,” Nature, vol. 333, pp. 363-364, 1988.
[19] A. C. Bovik, M. Clark, and W. S. Geisler, “Multichannel texture analysis using localized spatial filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 55-73, 1990.
[20] J. R. Bergen, “Theories of visual texture perception,” in Vision and Visual Dysfunction, vol. 10B, D. Regan, Ed., New York: MacMillan, 1991.
[21] N. Graham, “Complex channels, early local nonlinearities, and normalization in texture segregation,” in Computational Models of Visual Processing, M. S. Landy and J. A. Movshon, Eds., Cambridge, MA: MIT Press, 1991.
[22] M. R. Turner, “Texture discrimination by Gabor functions,” Biological Cybernetics, vol. 55, pp. 71-82, 1986.
[23] J. Malik and P. Perona, “Preattentive texture discrimination with early vision mechanisms,” Journal of the Optical Society of America A, vol. 7, pp. 923-932, 1990.
[24] A. K. Jain and F. Farrokhnia, “Unsupervised texture segmentation using Gabor filters,” Pattern Recognition, vol. 24, pp. 1167-1186, 1991.
[25] B. S. Manjunath and R. Chellapa, “A unified approach to boundary perception: Edges, texture, and illusory contours,” IEEE Transactions on Neural Networks, vol. 4, pp. 96-108, 1993.
[26] B. S. Manjunath and W. Y. Ma, “Texture features for browsing and retrieval of data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837-842, 1996.
[27] S. G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674-693, 1989.
[28] M. Unser and M. Eden, “Multiresolution feature extraction and selection for texture segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 717-728, 1989.
[29] H. W. Tang, V. Srinivasan, and S. H. Ong, “Texture segmentation via nonlinear interactions among Gabor feature pairs,” Optical Engineering, vol. 34, pp. 125-134, 1995.
[30] T. N. Tan, “Texture edge detection by modeling visual cortical channels,” Pattern Recognition, vol. 28, pp. 1283-1298, 1995.
[31] D. Dunn and W. E. Higgins, “Optimal Gabor filters for texture segmentation,” IEEE Transactions on Image Processing, vol. 4, pp. 947-964, 1995.
[32] T. Weldon and W. E. Higgins, “Designing multiple Gabor filters for multitexture image segmentation,” Optical Engineering, vol. 38, pp. 1478-1489, 1999.
[33] A. Teuner, O. Pichler, and B. J. Hosticka, “Unsupervised texture segmentation of images using tuned matched Gabor filters,” IEEE Transactions on Image Processing, vol. 4, pp. 863-870, 1995.
[34] O. Pichler, A. Teuner, and B. J. Hosticka, “An unsupervised texture segmentation algorithm with feature space reduction and knowledge feedback,” IEEE Transactions on Image Processing, vol. 7, pp. 53-61, 1998.
[35] J. G. Daugman, “Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, pp. 1169-1179, 1988.
[36] M. Porat and Y. Y. Zeevi, “The generalized Gabor scheme of image representation in biological and machine vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, pp. 452-468, 1988.
[37] W. McIlhagga, T. Hine, G. R. Cole, and A. W. Snyder, “Texture segregation with luminance and chromatic contrast,” Vision Research, vol. 30, pp. 489-495, 1990.
[38] T. V. Papathomas, R. S. Kashi, and A. Gorea, “A human vision based computational model for chromatic texture segregation,” IEEE Transactions on System, Man, and Cybernetics-Part B: Cybernetics, vol. 27, pp. 428-440, 1997.
[39] A. Jain and G. Healey, “A multiscale representation including opponent color features for texture recognition,” IEEE Transactions on Image Processing, vol. 7, pp. 124-128, 1998.
[40] M. Mirmehdi and M. Petrou, “Segmentation of color textures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 142-159, 2000.
[41] J. F. Camapum Wanderley and M. H. Fisher, “Multiscale color invariants based on the human visual system,” IEEE Transactions on Image Processing, vol. 10, pp. 1630-1638, 2001.
[42] L. G. Thorell, R. L. De Valois, and D. G. Albrecht, “Spatial mapping of monkey V1 cells with pure color and luminance stimuli,” Vision Research, vol. 24, pp. 751-769, 1984.
[43] A. Bradley, E. Switkes, and K. K. De Valois, “Orientation and spatial frequency selectivity of adaptation to colour and luminance gratings,” Vision Research, 28, pp. 841-856, 1988.
[44] M. A. Webster, K. K. De Valois, and E. Switkes, “Orientation and spatial-frequency discrimination for luminance and chromatic gratings,” Journal of the Optical Society of America A, vol. 7, pp. 1034-1049, 1990.
[45] E. Hering, Outlines of a theory of the light sense. Translated by L. M. Hurvish and D. Jameson. Cambridge, MA: Harvard University Press.
[46] S. A. Chen, “CNN-based texture boundary detection technique and its analog circuit implementation,” Master Thesis, National Chiao-Tung University, 2004.
[47] R. L. De Valois and K. K. De Valois, Spatial Vision. New York: Oxford University Press, 1988.
[48] D. H. Hubel, Eye, Brain, and Vision. Scientific American Library. New York: W. H. Freeman, 1988.
[49] S. W. Kuffler, “Discharge patterns and functional organization of mammalian retina,” Journal of Neurophysiology, vol. 16, pp. 37-68, 1953.
[50] H. B. Barlow, “Summation and inhibition in the frog’s retina,” Journal of Physiology, vol. 119, pp. 69-88, 1953.
[51] D. H. Hubel and T. N. Wiesel, “Receptive fields of single neurons in the cat’s striate cortex,” Journal of Physiology, vol. 148, pp. 574-591, 1959.
[52] D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction, and functional architecture of the visual cortex,” Journal of Physiology, vol. 160, pp. 106-154, 1962.
[53] D. H. Hubel and T. N. Wiesel, “Receptive fields and functional architecture of monkey striate cortex,” Journal of Physiology, vol. 195, pp. 215-243, 1968.
[54] F. W. Campbell and J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” Journal of Physiology, vol. 197, pp. 551-516, 1968.
[55] K. K. De Valois, R. L. De Valois, and E. W. Yund, “Responses of striate cortex cells to grating and checkerboard patterns,” Journal of Physiology, vol. 291, pp. 483-505, 1979.
[56] A. Bradley, E. Switkes, and K. K. De Valois, “Orientation and spatial frequency selectivity of adaptation to colour and luminance gratings,” Vision Research, 28, pp. 841-856, 1988.
[57] J. P. Jones and L. A. Palmer, “The two-dimensional spatial structure of simple receptive fields in cat striate cortex,” Journal of Neurophysiology, vol. 58, pp. 1186-1211, 1987.
[58] R. L. De Valois, I. Abramov, and G. H. Jacobs, “Analysis of response patterns of LGN cells,” Journal of the Optical Society of America, vol. 56, pp. 966-977, 1966.
[59] M. S. Livingstone and D. H. Hubel, “Segregation of form, color, movement and depth: Anatomy, physiology and perception,” Science, vol. 240, pp. 740-749, 1988.
[60] A. Treisman and G. Gelade, “A feature integration theory of attention,” Cognitive Psychology, vol. 12, pp. 97-136, 1980.
[61] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd Ed., Prentice Hall, 2002.
[62] J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679-698, 1986.
[63] I. Fogel and D. Sagi, “Gabor filters as texture discriminator,” Biological Cybernetics, vol. 61, pp. 103-113, 1989.
[64] S. Marcelja, “Mathematical description of the responses of simple cortical cells,” Journal of the Optical Society of America, vol. 70, pp. 1297-1300, 1980.
[65] D. A. Pollen and S. F. Ronner, “Phase relationships between adjacent simple cells in the visual cortex,” Science, vol. 212, pp. 1409-1411, 1981.
[66] J. J. Kulikowski, S. Marcelja, and P. O. Bishop, “Theory of spatial position and spatial frequency relations in the receptive field of simple cells in the visual cortex,” Biological Cybernetics, vol. 43, pp. 187-198, 1982.
[67] B. Sakitt and H. B. Barlow, “A model for the economical encoding of the visual image in cerebral cortex,” Biological Cybernetics, vol. 43, pp. 97-108, 1982.
[68] D. A. Pollen and S. F. Ronner, “Visual cortical neurons as localized spatial frequency filter,” IEEE Transactions on System, Man, and Cybernetics, vol. 13, pp. 907-916, 1983.
[69] S. G. Mallat, “Wavelets for a vision,” Proceedings of the IEEE, vol. 84, pp. 604-614, 1996.
[70] P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Transactions on Communications, vol. 31, pp. 532-540, 1983.
[71] CIE, Uniform Color Space—Color Difference Equations—Psychometric Color Terms. Commission Internationale de l'Eclairage, Publication No. 15, Supplement No. 2, Paris, 1978.
[72] E. Switkes, A. Bradley, and K. K. De Valois, “Contrast dependence and mechanisms of masking interactions among chromatic and luminance gratings,” Journal of the Optical Society of America A, vol. 5, pp. 1149-1162, 1989.
[73] M. Bach, C. Schmitt, T. Quenzer, T. Meigen, and M. Fahle, “Summation of texture segregation across orientation and spatial frequency: Electrophysiological and psychophysical findings,” Vision Research, vol. 40, pp. 3559-3566, 2000.
[74] E. N. Johnson, M. J. Hawken, and R. Shapley, “The spatial transform of color in the primary visual cortex of the macaque monkey,” Nature Neuroscience, vol. 4, pp. 409-416, 2001.
[75] D. Schluppeck and S. A. Engel, “Color opponent neurons in V1: A review and model reconciling results from imaging and single-uint recording,” Journal of Vision, vol. 2, pp. 480-492, 2002.
[76] R. Shapley and M. Hawken, “Neural mechanisms for color perception in the primary visual cortex,” Current Opinion in Neurobiology, vol. 12, pp. 426-432, 2002.
[77] A. Li and P. Lennie, “Mechanisms underlying segmentation of colored textures,” Vision Research, vol. 37, pp. 83-97, 1997.
[78] P. M. Pearson and F. A. A. Kingdom, “Texture-orientation mechanisms pool colour and luminance contrast,” Vision Research, vol. 42, pp. 1547-1558, 2002.
[79] K. T. Mullen and M. A. Losada, “Evidence for separate pathways for color and luminance detection mechanisms,” Journal of the Optical Society of America A, vol. 11, pp. 3136-3151, 1994.
[80] B. Poirson and B. Wandell, “Pattern-color separable pathways predict sensitivity to simple colored patterns,” Vision Research, vol. 36, pp. 515-526, 1996.
[81] N. Graham, J. Beck, and A. Sutter, “Nonlinear processes in spatial-frequency channel models of perceived texture segregation: Effects of sign and amount of contrast,” Vision Research, vol. 32, pp. 719-743, 1992.
[82] D. J. Heeger, “Normalization of cell responses in cat striate cortex,” Visual Neuroscience, vol. 9, pp. 181-197, 1992.
[83] D. G. Albrecht and R. L. De Valois, “Striate cortex responses to periodic patterns with and without the fundamental harmonics,” Journal of Physiology, vol. 319, pp. 495-514, 1981.
[84] J. Malik, S. Belongie, T. Leung, and J. Shi, “Contour and texture analysis for image segmentation,” International Journal of Computer Vision, vol. 43, pp. 7-27, 2001.
[85] D. R. Martin, C. C. Fowlkes, and J. Malik, “Learning to detect natural image boundaries using local brightness, color, and texture cues,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, pp. 530-549, 2004.
[86] P. Kruizinga and N. Petkov, “Computational models of visual neurons specialized in the detection of periodic and aperiodic oriented visual stimuli: Bar and grating cells,” Biological Cybernetics, vol. 76, pp. 83-96, 1997.
[87] P. Kruizinga and N. Petkov, “Nonlinear operator for oriented texture,” IEEE Transactions on Image Processing, vol. 8, pp. 1395-1407, 1999.
[88] S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Transactions on Image Processing, vol. 11, pp. 1160-1167, 2002.
[89] J. Rivest and P. Cavanagh, “Localizing contours defined by more than one attribute,” Vision Research, vol. 36, pp. 53-66, 1996.
[90] M. S. Landy and H. Kojima, “Ideal cue combination for localizing texture-defined edges,” Journal of the Optical Society of America A, vol. 18, pp. 2307-2320, 2001.
[91] P. V. McGraw, D. Whitaker, D. R. Badcock, and J. Skillen, “Neither here nor there: Localizing conflicting visual attributes,” Journal of Vision, vol. 3, pp. 265-273, 2003.
[92] University of Oulu Texture Database [Online]. Available: http://www.outex.oulu.fi/outex.php.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文