政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/49475

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/49475

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/49475

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/49475

Title:	基於延展式區域三元化圖型之特徵描述子 Feature descriptor based on extended local ternary pattern
Authors:	楊梃榮
Contributors:	廖文宏楊梃榮
Keywords:	區域二元化圖型延展式區域三元化圖型材質辨識 uniform pattern
Date:	2009
Issue Date:	2010-12-08 12:09:05 (UTC+8)
Abstract:	特徵描述子為電腦視覺中相當重要的一部分，本論文基於知名的特徵描述子：區域二元化圖型的架構上，提出了新的特徵描述子，並將其命名為延展式區域三元化圖型。我們所提出的特徵描述子與區域二元化圖型相比，有著較強的抗噪能力而且保留了區域二元化圖型簡單的計算複雜度。本論文也探討了區域三元化圖型中是否存在著uniform pattern，基於區域二元化圖型中uniform pattern的定義，我們提出了屬於區域三元化圖型的uniform pattern，並在圖像實驗中驗證了其大量存在性。我們將區域三元化圖型應用於材質分析與人臉辨識中，實驗結果驗證了本論文所提出的特徵描述法在這些應用的優越性。 Robust feature descriptor is essential in developing effective computer vision applications. In this thesis, we present an extension to the well-known local binary pattern (LBP) feature descriptor. The newly defined descriptor known as extended local ternary pattern (ELTP) exhibits better noise resistivity than the original LBP, while maintaining computational simplicity. We further investigate the presence of uniform patterns in ELTP. With a slight modification of the definition of uniformity, it is found experimentally that uniform ELTPs account for 80% of all patterns in texture images. The proposed ELTP and uniform ELTP are applied to object classification tasks, including texture analysis and face recognition. Experimental results validate the superiority of ELTP over conventional LBP approaches.
Reference:	[1] P. Viola and M. Jones, “Robust Real-Time Object Detection”. Proc. ICCV Second Int`l Workshop Statistical and Computational Theories of Vision Modelling, Learning, Computing, and Sampling, July 2001. [2] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection”, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR `05), vol. 1, pp. 886-893, 2005. [3] R.Plamondon and S.N. Srihari, “On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey”. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, Jan. 2000 [4] Google goggles www.google.com/mobile/goggles [5] VOC 2009 Challenge Results: http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2009/results/index.html [6] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int`l J. Computer Vision, vol. 2, no. 60, pp. 91-110, 2004. [7] B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada ,“Color and texture descriptors,” IEEE Trans. Circuit Syst. Video Technol., vol. 11, pp. 703–715, June 2001 [8] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, July 2002. [9] Pontil and A. Verri, “Support Vector Machines for 3D Object Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20(6), pp. 637-646, 1998. [10] A. Gionis, P. Indyk, and R. Motwani, “Similarity Search in High Dimensions via Hashing,” Proc. Very Large Data Base Conf. (VLDB `99), pp. 518–529, Sept. 1999. [11] T. Maenpaa, and M. Pictikainen, “Multi-scale binary patterns for texture analysis,” Springer Berlin / Heidelberg, 2003. [12] C. He, T. Ahonen and M. Pietikäinen, “A Bayesian Local Binary Pattern texture descriptor”,Proc. Intl Conf. on Pattern Recognition, 2008. [13] X. Tan and B. Triggs. “Enhanced local texture feature sets for face recognition under difficult lighting conditions”. In Analysis and Modeling of Faces and Gestures, volume 4778 of LNCS, pages 168–182. Springer, 2007 [14] Matthias Hein and Ulrike von Luxburg ,“Short Introduction to Spectral Clustering”, MLSS 2007 [15] Ng, A., Jordan, M., and Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. In T. Dietterich,S. Becker, and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 849 –856). MIT Press. [16] Brodatz database http://www.ux.uis.no/~tranden/ [17] T. Ahonen, A. Hadid, and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp.2037-2041, Dec. 2006. [18] The Yale Face Database B http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html [19] G. Zhao and M. Pietik¨ainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. PAMI, 29(6):915–928, 2007. [20] G.Zhao and M. Pietikäinen, “Dynamic Texture Recognition Using Volume Local Binary Patterns”, Proc. ECCV 2006 Workshop on Dynamical Vision, Graz, Austria, 2006, accepted. [21] M. Heikkil¨a, M. Pietik¨ainen, and C. Schmid, “Description of interest regions with center-symmetric local binary patterns”,In Computer Vision, Graphics and Image Processing, 5th Indian Conference, pages 58–69, 2006.
Description:	碩士國立政治大學資訊科學學系 97753027 98
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0097753027
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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