請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33684
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 貝蘇章 | |
dc.contributor.author | Chiao-Fen Hung | en |
dc.contributor.author | 洪巧芬 | zh_TW |
dc.date.accessioned | 2021-06-13T05:44:34Z | - |
dc.date.available | 2006-07-17 | |
dc.date.copyright | 2006-07-17 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-07-13 | |
dc.identifier.citation | Ch2
[1] L. Wang and D.C. He, ”Texture Classification Using Texture Spectrum,” Pattern Recognition, vol. 23, pp. 905-910, 1990. [2] T. Ojala, M. Pietikäinen and D. Harwood, ”A Comparative Study of Texture Measures with Classification Based on Feature Distributions,” Pattern Recognition, vol. 29, pp. 51-59, 1996. [3] T. Ojala, M. Pietikäinen and T. Mäenpää, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002. [4] T. Mäenpää, “The Local Binary Pattern Approach to Texture Analysis - Extensions and Applications,” Dissertation in Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, Finland, 2003. [5] M. Pietikäinen, T. Ojala and Z. Xu, “Rotation-Invariant Texture Classification Using Feature Distributions,” Pattern Recognition, vol. 33, pp. 43-52, 2000. [6] T. Mäenpää, T. Ojala, M. Pietikäinen and M. Soriano, ” Robust Texture Classification by Subsets of Local Binary Patterns,” Pattern Recognition, vol. 3, pp. 947-950, 2000. [7] T. Mäenpää, M. Pietikäinen and J. Viertola, “Separating Color and Pattern Information for Color Texture Discrimination,” Pattern Recognition, vol. 1, pp. 668-671, 2002. [8] A. Jain and G. Healey, “A Multiscale Representation Including Opponent Color Features for Texture Recognition,” IEEE Trans. Image Processing, vol. 7, pp. 124-128, 1998. [9] L. M. Hurvich and D. Jameson, “An Opponent-Process Theory of Color Process,” Physiol. Rev., vol. 64, pp. 384-404, 1957. Ch3 [1] R.M. Haralick and L.G. Shapiro, ”Computer and Robot Vision,” Addison-Wesley, 1992. [2] T. Mäenpää and M. Pietikäinen, “Texture Analysis with Local Binary Patterns,” Handbook of Pattern Recognition and Computer Vision, 3rd ed, World Scientific, pp. 197-216, 2005. [3] R.C. Gonzalez and R.E. Woods, ”Digital Image Processing,” Prentice-Hall, 2nd ed,2002 [4] G.D. Finlayson, B. Schiele and J.L. Crowley, “Comprehensive Colour Image Normalization,” European Conference on Computer Vision (ECCV), vol. 1, pp. 475-490, 1998. [5] T. Gevers and A.W.M. Smeulders, ”Color Constant Ratio Gradients for Image Segmentation and Similarity of Texture Objects,” Computer Vision and Pattern Recognition, vol. 1, pp. 18-25, 2001. [6] T. Mäenpää, M. Pietikäinen and J. Viertola, “Separating Color and Pattern Information for Color Texture Discrimination,” Pattern Recognition, vol. 1, pp. 668-671, 2002. [7] M. Swain and D. Ballard, “Color Indexing,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, pp. 522-529, 1995. [8] www.oulu.edu.fi [9] R.W.G. Hunt, ” Measuring Color,” Halsted Press, New York, 1989. [10] T. Mäenpää and M. Pietikäinen, “Classification with Color and Texture: Jointly or Separately?” Pattern Recognition, vol. 37, pp. 1629-1640, 2004. Ch4 [1] M. Turk and A. Pentland, “Eigenfaces for Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 3, pp. 71-86, 1991. [2] K. Etemad and R. Chellappa, “Discriminant Analysis for Recognition of Human Face Images,” Journal of the Optical Society of America A-Optics Image Science and Vision, vol. 14, pp. 1724-1733, 1997. [3] R.O. Duda, P.E. Hart and D.G.Stork, “Pattern Classification,” John Wiley & Sons., 2nd ed, 2000. [4] L. Wiskott, J.M. Feellous, N. Kruger and C. Malsburg,“Face Recognition by Elastic Bunch Graph Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 1, pp. 26-29,1997. [5] Moghaddam, Nastar and Pentland” A Bayesian Similarity Measure for Direct Image Matching,” Pattern Recognition, vol. 2,pp. 350-358, 1996 [6] M.H. Yang, D.J. Kriegman and N. Ahuja,“Detecting Faces in Images: a Aurvey,” IEEE trans. Pattern Analysis and Machine Intelligence, vol. 24, pp. 34-58, 2002. [7] J. Miao, B Yin, K Wang, L Shen and X Chen, “A Hierarchical Multiscale and Multiangle System for Human Face Detection in a Complex Background Using Gravity-Center Template,” Pattern Recognition, vol. 32, pp. 1237-1248, 1999. [8] A. Hadid, M. Pietikäinen and T. Ahonen, ”A Discriminative Feature Space for Detecting and Recognition Faces,” Computer Vision and Pattern Recognition, vol. 2, pp. 797-804, 2004. [9] http://cswww.essex.ac.uk/mv/allfaces/faces95.html [10] H.Y. Mark Liao, C.C. Han and G.J. Yu, “Face + Hair + Shoulders + Background Face,” Proc. Workshop on 3D Computer Vision, invited paper, 1997. [11] T. Ahonen, A. Hadid and M. Pietikäinen, “Face Recognition with Local Binary Patterns,” European Conference on Computer Vision (ECCV), vol. 1, pp. 469-481, 2004. [12] W. Zhang, S. Shan,W Gao, X.Chen and H. Zhanh, “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition,” International Conference on Computer Vision (ICCV), vol. 1, pp. 786-791, 2005. [13] G. Heusch, Y. Rodriguez and S. Marcel, “Local Binary Patterns as an Image Preprocessing for Face Authentication,” Automatic Face and Gesture Recognition (AFGR), pp. 9-14, 2006. Ch5 [1] R.J. Radke, S. Andra, O. Al-Kofahi and B. Roysam, “Image Change Detection Algorithms: A System Survey,” IEEE Trans. Image Processing, vol. 14, pp. 294-307, 2005. [2] M. Heikkilä and M. Pietikäinen, “A Texture-Based Method for Modeling the Background and Detecting Moving Objects,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, pp. 657-662, 2006. [3] P. Rosin, “Thresholding for Change Detection,” Computer Vision, vol. 86, pp.79-95, 2002. [4] P. Rosin and E. Ioannidis, “Evaluation of Global Image Thresholding for Change Detection,” Pattern Recognition Lett., vol. 24, pp.2345-2356, 2003. [5] H.V. Poor, “An Introduction to Signal Detection and Estimation,” Springer-Verlag, New York, 2nd ed, 1994. [6] S. M. Key, “Fundamentals of Statistical Signal Processing: Detection Theory,” Upper Saddle River, NJ: Prentic-Hall, 1993. [7] C. Stauffer and W.E.L. Grimson, “Adaptive Background Mixture Models for Real-Time Tracking,” Computer Vision and Pattern Recognition, vol. 2, pp. 23-25, 1999. [8] C. Stauffer and W.E.L.Grimson, “Learning Patterns of Activity Using Real-Time Tracking” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, pp. 747-757, 2000. [9] M. Heikkilä, M. Pietikäinen and J. Heikkilä, ”A Texture-Based Method for Detecting Moving Objects,” British Machine Vision Conference (BMVC), vol. 1,pp. 187-196, 2004. [10] R.M. Haralick and L.G. Shapiro, ”Computer and Robot Vision,” Addison-Wesley, 1992. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33684 | - |
dc.description.abstract | 本篇論文呈現了著名的材質分析運算法:區域性二元化圖形 (local binary patterns,LBP),其統計圖構成了一個材質分析上重要的特徵。LBP 是由材質區域性的定義所導出,具有不受影像灰階值比例改變而變化的算子;同時它也被發展成擁有不依影像旋轉而改變的特性。且為了適合不同的應用,LBP延伸出各式各樣的形式。
我們不只介紹LBP基礎的理論,更著重於LBP在影像和視訊上的著名應用。材質分析是LBP最傳統的用法,我們評估了多種不同以LBP為基礎,且包含色彩資訊的特徵,來分辨68種不同的材質。臉部偵測及辨識是另一個由於LBP對材質有利的分辨能力而衍生出的應用,我們用同一種特徵來統一臉部定位及辨識。最後,偵測移動物體是LBP最新的應用,經由依序列的可適性LBP統計圖,我們可以建構背景的模型以及分割出有移動的物體。我們實現了這個方法,並給予一些建議。 | zh_TW |
dc.description.abstract | This thesis presents the local binary patterns, the well-know operator of texture analysis. The LBP histogram constructs an important feature for texture. The operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. It is made invariant against the rotation of the image domain. Various extensions of LBP have been developed to get preferable characteristics.
We introduce not only the fundamental theory but also the noticeable applications in image and video analysis with LBP. The texture analysis is a traditional use of LBP. We evaluate the performance of several LBP-based features included color information to classify 68 difference kinds of textures. Face detection and recognition is another utilization of LBP due to the powerful distinguishing ability of texture. We unified both face detection and recognition with the same feature. Finally, the newest application is moving object detection with LBP. Via using a sequence of adaptive LBP histograms, we can model the background and detect objects with movement. This method is implemented and some advice is given. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T05:44:34Z (GMT). No. of bitstreams: 1 ntu-95-R93942090-1.pdf: 2260216 bytes, checksum: 55f1b073d1d871b0742ea4d178d024c6 (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | Chapter 1 Introduction………………………………….. 1
Chapter 2 Theory of Local Binary Patterns………………………….……….. 3 2.1 Introduction …………………………………………………………… 3 2.2 Basic Methodology of LBP……………………………………………. 4 2.2.1 LBP Calculation………………………………………………… 4 2.2.2 Physical Meanings of LBP……………………………………… 6 2.3 Derivation and Properties of LBP…………………..………………….... 7 2.3.1 Gray-Scale Invariance Property……………………………........ 8 2.3.2 Rotation Invariance Property………………………………….... 9 2.4 Extensions………………..……………………………………………… 11 2.4.1 Uniform LBP……………………………………………………. 11 2.4.2 Multi-resolution LBP………………………………………….... 11 2.4.3 Opponent Color LBP……………………………………………. 13 Chapter 3 Texture Analysis with Local Binary Patterns……………………... 17 3.1 Introduction ……………………………………………………………... 17 3.2 Feature Extraction……………………………………………………...... 19 3.2.1 Color Feature Extraction: Color Histogram………..…………… 19 3.2.2 Texture Feature Extraction……………………………………… 21 3.3 Jointly and Separately Classification with Color and Texture…………... 22 3.3.1 Jointly: LBP on Multi-Spectra………………………………...... 22 3.3.2 Separately: LBP and Color Histogram………………………….. 23 3.4 Dissimilarity Measure…………………………………………………… 25 3.5 Experiment Result: Classification on Outex 13/14 Database…….……... 27 3.5.1 Outex Color Texture Database………………………………….. 27 3.5.2 Experiment Setup……………………………………………...... 28 3.5.3 Result and Discussion………………………………………… 30 3.6 Conclusions…………………………………………………………….... 32 Chapter 4 Face Representation and Recognition with Local Binary Patterns............................................................................................... 35 4.1 Introduction……………………………………………..………..……… 35 4.2 Review of Face detection…………………………………………..……. 37 4.2.1 Background……….………………………………………..…… 37 4.2.2 Related Work……………………………………………….....… 39 4.3 Related Work of Face Recognition with LBP…………………………… 41 4.3.1 Weighted Block LBP Histogram………………………………... 41 4.3.2 Local Gabor Binary Pattern Histogram Sequence……………… 42 4.3.3 LBP as an Image Preprocessing……………………………….... 43 4.4 Experiment Design from Face Localization to Face Recognition............. 43 4.4.1 Proposed Method for Face Localization………………………... 44 4.4.2 Face Recognition………………………………………………... 46 4.5 Experiment Result and Discussion…………..……………………....…... 47 Chapter 5 Motion Analysis with Local Binary Patterns..…………………….. 51 5.1 Introduction……………………………………………………………… 51 5.2 Review of Related Work………………………………………………… 53 5.2.1 Temporal Differencing…………………………………...……... 53 5.2.2 Background Subtracting………………………………………… 54 5.3 Texture Based-Method Motion Detection Using LBP…………………... 56 5.3.1 Initial Setting……………………………………………………. 57 5.3.2 Background Model Updating…………………………………… 58 5.3.3 Background Selection and Foreground Detection………............ 58 5.3.4 Pixel Version of This Method…………………………………… 59 5.4 Implementation and Parameter Choices……………………………......... 60 5.4.1 Parameter Statements…………………………………………… 60 5.4.2 Experimental Results……………………………………………. 62 5.5 Conclusion………………………………………………..…………........ 67 Chapter 6 Conclusions and Future Work…………………………………….. 69 6.1 Conclusions ……………………………………………………………... 69 6.2 Future Work…….……………………………………………………...... 70 References……………………………………………………………..…………. 72 | |
dc.language.iso | en | |
dc.title | 區域性二元化圖形對於影像及視訊之分析 | zh_TW |
dc.title | Local Binary Patterns for Image and Video Analysis | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃文良,祈忠勇 | |
dc.subject.keyword | 影像分析,視訊分析, | zh_TW |
dc.subject.keyword | Local binary pattern,image analysis, | en |
dc.relation.page | 76 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2006-07-16 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-95-1.pdf 目前未授權公開取用 | 2.21 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。