臺灣博碩士論文加值系統

隨著科技時代的到來，電腦視覺被廣泛地應用在許多領域，例如，
臉部辨識、物件偵測、影像檢索和監控系統。在影像處理中，特徵擷
取是不可或缺的步驟之一，經由特徵擷取能夠從一群龐大的資料中，
選擇出真實反映影像資訊的成分。我利用兩個演算法，分別為局部二
值模式以及韋伯局部描述子，進行臉部辨識以及中國書法作家辨認的
實驗。局部二值模式是利用空間灰階共生的方法，有效率地計算局部
範圍內中心像素與周圍鄰近像素間的亮度關係，來形成一特徵灰階圖。
韋伯局部描述子則是受到韋伯定理的啟發，定理指出個體對兩個重量
（或聲、光等）之間的差異，須在這個差異達到可辨識的程度，即所謂
的「恰好可分辨的差異度」(just noticeable difference, JND)，才能有所覺察，此演算法被認為是較符合人類感知的方法。
除此之外，我修改了局部二值模式演算法中的缺點，並且結合傳
統局部二值模式的方法與方向性的概念，形成一個更強大的描述子，
稱為“程度方向差異局部二值描述子”。此外，過去研究大部份將局部二
值模式的方法應用於灰階的影像中，我嘗試將此演算法應用於彩色影
像上，也保留傳統局部二值模式演算法的精神，並加入色彩資訊，稱
為“HSV色彩空間-局部二值模式”。我提出的方法顯示在彩色影像中，
能夠擷取出更清楚的輪廓特徵。
最後一部分，則是著重在彩色與灰階影像的轉換，我提出的演算
法不僅能夠在灰階影像上保留原彩色影像中的輪廓，更能達到加強對
比性的效果。

With the advent of the technological era, computer vision is used widely in many fields such as face recognition, object detection, image retrieval and surveillance systems. In image processing, feature extraction is an indispensable step, which can reflect the intrinsic content (information) from the images (data). I utilized Local Binary Pattern (LBP) and Weber Local Descriptor
(WLD), these two powerful descriptors to do experiments including face recognition, and Chinese Calligraphy Recognition. LBP is a spatial gray-level dependence method (co-occurrence method) and can be computed efficiently by thresholding the neighborhood of each pixel with the center pixel value to form a gray-scale invariant pattern. Weber local descriptor was inspired by
Weber’s Law and was deemed to base on the fact of human perception.
Besides, I revised the disadvantage of Local Binary Pattern algorithm and made a combination of conventional LBP with direction information to form a robust descriptor named “Magnitude and Direction Difference Local Binary Descriptor”. Furthermore, Local Binary Pattern was used to apply to grayscale images. I attempted to make use of the descriptor on color images. I also revised the traditional method but preserving the spirit when dealing with color images, called “HSV-LBP”. My results show that the revised version can extract clear features than previous LBP on color images.
Last, I put emphasis on the topic about converting color images to grayscale images and not only preserving contrast but enhancing contrast.

口試委員會審定書ii
誌謝iii
摘要iv
Abstract v
1 Introduction 1
1.1 Feature Extraction in Computer Vision . . . . . . . . . . . . . . . . . . . 1
1.2 Algorithms for Feature Extraction . . . . . . . . . . . . . . . . . . . . . 2
2 Local Binary Pattern 4
2.1 Local Binary Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Uniform and Non-uniform LBP . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Rotation Invariant LBP and Uniform Rotation Invariant LBP . . . . . . . 8
2.4 Multi-scale Local Binary Pattern . . . . . . . . . . . . . . . . . . . . . . 13
3 Weber Local Descriptor 15
3.1 Micro Structure Representation in Excitation Domain . . . . . . . . . . . 17
3.2 Orientation Component . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3 WLD Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 Weber Local Binary Pattern 20
4.1 Comparison with Weber Local Descriptor and Local Binary Pattern . . . 20
4.2 Weber Local Binary Pattern Descriptor . . . . . . . . . . . . . . . . . . . 21
4.3 WLBP Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5 Magnitude and Direction Difference for LBP and WLBP 24
5.1 The Concept of Magnitude and Direction Difference . . . . . . . . . . . 24
5.2 Magnitude and Direction Difference for LBP and Uniform LBP . . . . . 29
5.3 Magnitude and Direction Difference for WLBP . . . . . . . . . . . . . . 30
6 Experiments 32
6.1 Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
6.1.1 2D Grayscale Images . . . . . . . . . . . . . . . . . . . . . . . . 34
6.1.2 Depth Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
6.1.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
6.2 Calligraphy Writer Identification . . . . . . . . . . . . . . . . . . . . . . 43
6.2.1 Binarized LBP Operator . . . . . . . . . . . . . . . . . . . . . . 44
6.2.2 Classifier-SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
6.2.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
6.2.4 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6.2.5 Revised Training Model and Future Work . . . . . . . . . . . . . 47
7 HSV-LBP on color images 48
7.1 Introduction to HSV-LBP . . . . . . . . . . . . . . . . . . . . . . . . . . 48
7.2 Algorithm of HSV-LBP on color images . . . . . . . . . . . . . . . . . . 49
7.2.1 Hue Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
7.2.2 Saturation Domain . . . . . . . . . . . . . . . . . . . . . . . . . 53
7.2.3 Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
7.3 Results of HSV-LBP in comparison with LBP . . . . . . . . . . . . . . . 54
7.4 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
8 Color Image to Grayscale Conversion 58
8.1 Introduction to convert Color Image to Grayscale . . . . . . . . . . . . . 58
8.2 Contrast Preserving and Enhancing using HSV color Space . . . . . . . . 60
8.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
8.3.1 Procedure and result . . . . . . . . . . . . . . . . . . . . . . . . 65
8.3.2 Comparison with other grayscale methods . . . . . . . . . . . . . 65
8.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
9 Conclusion and Future Work 73
Bibliography 75

[1] Chinese calligraphy database. http://163.20.160.14/~word/modules/myalbum/index.php.
[2] The japanese female facial expression (jaffe) database. http://www.kasrl.org/jaffe.html.
[3] T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 28(12):2037–2041, Dec. 2006.
[4] T. Ahonen, J. Matas, C. He, and M. Pietikainen. Rotation invariant image description with local binary pattern histogram fourier features. In Proceedings of the 16th Scandinavian Conference on Image Analysis, SCIA ’09, pages 61–70, Berlin, Heidelberg, 2009. Springer-Verlag.
[5] T. Ahonen, M. Pietikainen, A. Hadid, and T. Maenpaa. Face recognition based on the appearance of local regions. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 3, pages 153–156 Vol.3, Aug 2004.
[6] A. Aissaoui, J. Martinet, and C. Djeraba. Dlbp: A novel descriptor for depth image based face recognition. In 2014 IEEE International Conference on Image Processing (ICIP), pages 298–302, Oct 2014.
[7] C. Chen and S. Miaou. Global variable adaptive local ternary pattern (gvaltp ) with a fast calculation mechanism.
[8] J. Chen, S. Shan, C. He, G. Zhao, M. Pietikainen, X. Chen, and W. Gao. Wld: A robust local image descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1705–1720, Sept 2010.
[9] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley-Interscience, 2001.
[10] M. Fairchild. Color Appearance Models. The Wiley-IS&T Series in Imaging Science and Technology. Wiley, 2005.
[11] K. Fukunaga. Introduction to Statistical Pattern Recognition (2Nd Ed.). Academic Press Professional, Inc., San Diego, CA, USA, 1990.
[12] D. Gong, S. Li, and Y. Xiang. Face recognition using the weber local descriptor. In The First Asian Conference on Pattern Recognition, pages 589–592, Nov 2011.
[13] S. Gupta, M. K. Markey, and A. C. Bovik. Texas 3d face recognition database. http://www.kasrl.org/jaffe.html.
[14] S. Gupta, M. K. Markey, and A. C. Bovik. Anthropometric 3d face recognition. International Journal of Computer Vision, 90(3):331–349, 2010.
[15] P. Hough. Method and Means for Recognizing Complex Patterns. U.S. Patent 3.069.654, Dec. 1962.
[16] A. K. Jain. Fundamentals of Digital Image Processing. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1989.
[17] I. T. Jolliffe. Principal Component Analysis. Springere, New York, 2 edition, 2002.
[18] K. Kiratiratanapruk and W. Sinthupinyo. Color and texture for corn seed classification by machine vision. In Intelligent Signal Processing and Communications Systems (ISPACS), 2011 International Symposium on, pages 1–5, Dec 2011.
[19] F. Liu, Z. Tang, and J. Tang. WLBP: weber local binary pattern for local image description. Neurocomputing, 120:325–335, 2013.
[20] D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60(2):91–110, Nov. 2004.
[21] C. Lu, L. Xu, and J. Jia. Contrast preserving decolorization with perception-based quality metrics. Int. J. Comput. Vision, 110(2):222–239, Nov. 2014.
[22] A. Nicolaou, M. Liwicki, and R. Ingold. Oriented local binary patterns for writer identification. In M. I. Malik, M. Liwicki, L. Alewijnse, M. Blumenstein, C. Berger, R. Stoel, and B. Found, editors, Proceedings of the 2nd ICDAR International Workshop on Automated Forensic Handwriting Analysis, AFHA 2013, Washington DC, USA, 22-23 August 2013, volume 1022 of CEUR Workshop Proceedings, pages 15–20. CEUR-WS.org, 2013.
[23] T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971–987, Jul 2002.
[24] T. Ojala, M. Pietikainen, and D. Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51–59, 1996.
[25] P. S. Penev and J. J. Atick. Local feature analysis: A general statistical theory for object representation, 1996.
[26] A. Pentland, B. Moghaddam, and T. Starner. View-based and modular eigenspaces for face recognition. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR ’94., 1994 IEEE Computer Society Conference on, pages 84–91, Jun 1994.
[27] M. Pietikainen, T. Ojala, and Z. Xu. Rotation-invariant texture classification using feature distributions. Pattern Recognition, 33:43–52, 2000.
[28] G. Sharma. Digital Color Imaging Handbook. CRC Press, Inc., Boca Raton, FL, USA, 2002.
[29] A. Suruliandi, K. Meena, and R. R. Rose. Local binary pattern and its derivatives for face recognition. IET Computer Vision, 6(5):480–488, Sept 2012.
[30] C. F. Tsai and W. C. Lin. A comparative study of global and local feature representations in image database categorization. In INC, IMS and IDC, 2009. NCM ’09. Fifth International Joint Conference on, pages 1563–1566, Aug 2009.
[31] M. Turk and A. Pentland. Eigenfaces for recognition. J. Cognitive Neuroscience, 3(1):71–86, 1991.
[32] J. Yang and X. Zhang. Feature-level fusion of global and local features for fingervein recognition. In IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, pages 1702–1705, Oct 2010.
[33] S. Yin, X. Dai, P. Ouyang, L. Liu, and S. Wei. A multi-modal face recognition method using complete local derivative patterns and depth maps. Sensors, 14(10):19561, 2014.
[34] B. Zhang, Y. Gao, S. Zhao, and J. Liu. Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing, 19(2):533–544, Feb 2010.