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題名 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
作者 楊梃榮
貢獻者 廖文宏
楊梃榮
關鍵詞 區域二元化圖型
延展式區域三元化圖型
材質辨識
uniform pattern
日期 2009
上傳時間 8-Dec-2010 12:09:05 (UTC+8)
摘要 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.
參考文獻 [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. Int’l 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.
描述 碩士
國立政治大學
資訊科學學系
97753027
98
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0097753027
資料類型 thesis
dc.contributor.advisor 廖文宏zh_TW
dc.contributor.author (Authors) 楊梃榮zh_TW
dc.creator (作者) 楊梃榮zh_TW
dc.date (日期) 2009en_US
dc.date.accessioned 8-Dec-2010 12:09:05 (UTC+8)-
dc.date.available 8-Dec-2010 12:09:05 (UTC+8)-
dc.date.issued (上傳時間) 8-Dec-2010 12:09:05 (UTC+8)-
dc.identifier (Other Identifiers) G0097753027en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/49475-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 97753027zh_TW
dc.description (描述) 98zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 第一章 研究背景與目的 1
第二章 相關研究 4
2.1 區域二元化圖型 4
2.2 區域三元化圖型 7
第三章 延展式區域三元化圖型 10
3.1 三元化的範圍設定 10
3.2 樣式編碼與轉換方式 12
3.3 Spectral Clustering 19
3.3.1 分群簡介 19
3.3.2 Spectral Clustering的概念 20
3.3.3 Graph Laplacian Matrix 22
3.3.4 Spectral Clustering 演算法 23
3.4 分群結果 25
第四章 ELTP 中的Uniform Patterns 29
4.1 LBP中的Uniform Pattern 29
4.2 ELTP中的Uniform Pattern 30
4.3 Uniform Pattern的降維 33
第五章 抗噪實驗 36
5.1 抗噪力實驗(一):加入高斯雜訊 36
5.2 抗噪力實驗(二):光影變化 40
5.3 抗噪力實驗(三):加入不同強度雜訊 43
第六章 延展式區域三元化圖型之應用 49
6.1 材質分類 49
6.1.1 材質分類實驗結果 50
6.1.2 材質分類實驗結果(二) 56
6.2 人臉辨識 59
6.2.1 實驗結果 60
第七章 結論與後續研究改進方向 63
參考文獻 64
附錄A材質分類實驗結果(二)數據 67		zh_TW
dc.format.extent 3323494 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0097753027en_US
dc.subject (關鍵詞) 區域二元化圖型zh_TW
dc.subject (關鍵詞) 延展式區域三元化圖型zh_TW
dc.subject (關鍵詞) 材質辨識zh_TW
dc.subject (關鍵詞) uniform patternen_US
dc.title (題名) 基於延展式區域三元化圖型之特徵描述子zh_TW
dc.title (題名) Feature descriptor based on extended local ternary patternen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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. 2000zh_TW
dc.relation.reference (參考文獻) [4] Google goggleszh_TW
dc.relation.reference (參考文獻) www.google.com/mobile/goggleszh_TW
dc.relation.reference (參考文獻) [5] VOC 2009 Challenge Results:zh_TW
dc.relation.reference (參考文獻) http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2009/results/index.htmlzh_TW
dc.relation.reference (參考文獻) [6] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int`l J. Computer Vision, vol. 2, no. 60, pp. 91-110, 2004.zh_TW
dc.relation.reference (參考文獻) [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 2001zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [11] T. Maenpaa, and M. Pictikainen, “Multi-scale binary patterns for texture analysis,” Springer Berlin / Heidelberg, 2003.zh_TW
dc.relation.reference (參考文獻) [12] C. He, T. Ahonen and M. Pietikäinen, “A Bayesian Local Binary Patternzh_TW
dc.relation.reference (參考文獻) texture descriptor”,Proc. Int’l Conf. on Pattern Recognition, 2008.zh_TW
dc.relation.reference (參考文獻) [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, 2007zh_TW
dc.relation.reference (參考文獻) [14] Matthias Hein and Ulrike von Luxburg ,“Short Introduction to Spectral Clustering”, MLSS 2007zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [16] Brodatz databasezh_TW
dc.relation.reference (參考文獻) http://www.ux.uis.no/~tranden/zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [18] The Yale Face Database Bzh_TW
dc.relation.reference (參考文獻) http://cvc.yale.edu/projects/yalefacesB/yalefacesB.htmlzh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW