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題名 中心對稱式延展區域三元化圖型特徵描述子
Center-Symmetric Extended Local Ternary Patterns
作者 劉嘉瑜
貢獻者 廖文宏
劉嘉瑜
關鍵詞 延展式區域三元化圖型
中心對稱式延展區域三元化圖型
混合式描述方式
物件辨識
特徵描述子
日期 2013
上傳時間 10-Feb-2014 14:57:07 (UTC+8)
摘要 物件辨識是電腦視覺領域中相當重要的一環，在許多應用中皆可看到物件辨識的身影，而物件辨識的關鍵在於描述物件特徵的描述子。本論文基於「延展式區域三元化圖型」，提出一種新的特徵描述子，稱為「中心對稱式延展區域三元化圖型」，改善繁複的編碼過程，在辨識力、抗噪力，以及處理效率三者之間達到良好的平衡。除此之外，我們也將不同描述子特性加以融合，稱為「混合式描述方式」，實驗結果證實在高雜訊的材質影像中，「混合式描述方式」提升了辨識力以及抗噪力。
Object recognition is an important problem in computer vision. Effective recognition of objects calls for the appropriate selection of feature descriptor. In this thesis, we generalize the “Extended Local Ternary Patterns” (ELTP) to form a novel and compact set of features named Center-Symmetric Extended Local Ternary Patterns (CS-ELTP). The newly defined CS-ELTP requires a simplified encoding procedure and has a lower dimension for a fixed neighborhood region. It achieves good balance among feature dimension, recognition rate and noisy resistance according to our comparative experimental analysis. In addition, we combine binary and ternary patterns to create a hybrid descriptor that possesses the characteristics of both types of descriptor. Experimental results indicate that the hybrid descriptor can improve the performance in noisy conditions while maintaining a reasonable feature size.
參考文獻 [1] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int`l J. Computer Vision, vol. 2, no. 60, pp. 91-110, 2004.
[2] 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.
[3] 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.
[4] P. Viola and M. Jones, “Robust Real-Time Object Detection”. Proc. ICCV Second Int`l Workshop Statistical and Computational Theories of Vision Modeling, Learning, Computing, and Sampling, July 2001.
[5] 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
[6] W. H. Liao, “Region Description Using Extended Local Ternary Patterns”, Proceedings of the 20th International Conference on Pattern Recognition, pp. 1003-1006, 2010.
[7] Marko Heikkil¨a, Matti Pietik¨ainen, and Cordelia Schmid, “Description of Interest Regions with Center-Symmetric Local Binary Patterns”, Computer Vision, Graphics and Image Processing(CVPR `06), Volume 4338, pp 58-69, 2006.
[8] 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.
[9] A. Shobeirinejad and Y. S. Gao, “Gender Classification Using Interlaced Derivative Patterns“, Proceedings of the 20th International Conference on Pattern Recognition, pp. 1509-1512, 2010.
[10] M. Heikkilä, M. Pietikäinen and C. Schmid, “Description of Interest Regions with Center-Symmetric Local Binary Patterns”, Computer Vision, Graphics and Image Processing, Lecture Notes in Computer Science, 2006.
[11] J. C. Dunn "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics 3: 32-57, 1973.
[12] Timo Ojala, Topi Mäenpää, Matti Pietikäinen, Jaakko Viertola, Juha Kyllönen and Sami Huovinen, “Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms”, 21st International Conference on Pattern Recognition, November 11-15, 2012.
[13] Alexander Strehl and Joydeep Ghosh, “Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions”, Journal of Machine Learning Research 3, 583-617,2002.
[14] Ulrike von Luxburg, “A Tutorial on Spectral Clustering”, Statistics and Computing, 17 (4), 2007.
[15] Wen-Hung Liao, “Commensurate dimensionality reduction for extended local ternary patterns”, International Conference on Pattern Recognition (ICPR), 2012.
[16] Xiaosheng Wu and Junding Sun, “An Extended Center-Symmetric Local Ternary Patterns for Image Retrieval”, International Conference, CSEE 2011.
描述 碩士
國立政治大學
資訊科學學系
100753015
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100753015
資料類型 thesis
dc.contributor.advisor 廖文宏zh_TW
dc.contributor.author (Authors) 劉嘉瑜zh_TW
dc.creator (作者) 劉嘉瑜zh_TW
dc.date (日期) 2013en_US
dc.date.accessioned 10-Feb-2014 14:57:07 (UTC+8)-
dc.date.available 10-Feb-2014 14:57:07 (UTC+8)-
dc.date.issued (上傳時間) 10-Feb-2014 14:57:07 (UTC+8)-
dc.identifier (Other Identifiers) G0100753015en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/63711-
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) 100753015zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 物件辨識是電腦視覺領域中相當重要的一環，在許多應用中皆可看到物件辨識的身影，而物件辨識的關鍵在於描述物件特徵的描述子。本論文基於「延展式區域三元化圖型」，提出一種新的特徵描述子，稱為「中心對稱式延展區域三元化圖型」，改善繁複的編碼過程，在辨識力、抗噪力，以及處理效率三者之間達到良好的平衡。除此之外，我們也將不同描述子特性加以融合，稱為「混合式描述方式」，實驗結果證實在高雜訊的材質影像中，「混合式描述方式」提升了辨識力以及抗噪力。zh_TW
dc.description.abstract (摘要) Object recognition is an important problem in computer vision. Effective recognition of objects calls for the appropriate selection of feature descriptor. In this thesis, we generalize the “Extended Local Ternary Patterns” (ELTP) to form a novel and compact set of features named Center-Symmetric Extended Local Ternary Patterns (CS-ELTP). The newly defined CS-ELTP requires a simplified encoding procedure and has a lower dimension for a fixed neighborhood region. It achieves good balance among feature dimension, recognition rate and noisy resistance according to our comparative experimental analysis. In addition, we combine binary and ternary patterns to create a hybrid descriptor that possesses the characteristics of both types of descriptor. Experimental results indicate that the hybrid descriptor can improve the performance in noisy conditions while maintaining a reasonable feature size.en_US
dc.description.tableofcontents 第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 1
1.3 論文架構 3
第二章 相關研究 4
2.1 Local Binary Patterns, LBP 4
2.2 Center-Symmetric Local Binary Patterns, CS-LBP 8
2.3 Local Ternary Patterns, LTP 9
2.4 Extended Local Ternary Patterns, ELTP 10
2.5 Extended Center-Symmetric Local Ternary Pattern, eCS-LTP 12
2.6 其他各種降維方式 13
2.6.1 合併直方圖相鄰樣式 13
2.6.2 減少樣本數量 14
2.6.3 旋轉不變的特性(rotational-invariance) 14
第三章 中心對稱式延展區域三元化特徵描述子 16
3.1 中心對稱式延展區域三元化特徵描述子 16
3.2 CS-ELTP中的Uniform Patterns 17
3.3 Spectral Clustering分群演算法 18
3.4 分類方法及材質影像來源 19
3.5 CS-ELTP的Uniform pattern 20
3.5.1 Uniform pattern代表的意義 20
3.5.2 LBP中的Uniform pattern 22
3.5.3 CS-ELTP中的Uniform pattern 23
第四章 材質影像分類實驗 27
4.1 原始取樣定義描述子的材質分類 27
4.2 經過降維描述子的材質分類 30
4.3 抗噪力實驗 33
4.3.1 抗噪力實驗一：材質加入雜訊強度20dB的高斯雜訊 33
4.3.2 抗噪力實驗二：材質加入雜訊強度40dB的高斯雜訊 35
4.3.3 抗噪力實驗三：比較降維前後的抗噪力 37
4.3.4 辨識錯誤原因探討 40
第五章 混合式描述子的融合實驗 49
5.1 融合的方式 49
5.2 未降維描述子融合實驗 51
5.3 經降維描述子融合實驗 53
5.4 調整混合式描述子融合比重 55
第六章 結論與未來研究方向 60
參考文獻 66		zh_TW
dc.format.extent 6049774 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/#G0100753015en_US
dc.subject (關鍵詞) 延展式區域三元化圖型zh_TW
dc.subject (關鍵詞) 中心對稱式延展區域三元化圖型zh_TW
dc.subject (關鍵詞) 混合式描述方式zh_TW
dc.subject (關鍵詞) 物件辨識zh_TW
dc.subject (關鍵詞) 特徵描述子zh_TW
dc.title (題名) 中心對稱式延展區域三元化圖型特徵描述子zh_TW
dc.title (題名) Center-Symmetric Extended Local Ternary Patternsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int`l J. Computer Vision, vol. 2, no. 60, pp. 91-110, 2004.
[2] 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.
[3] 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.
[4] P. Viola and M. Jones, “Robust Real-Time Object Detection”. Proc. ICCV Second Int`l Workshop Statistical and Computational Theories of Vision Modeling, Learning, Computing, and Sampling, July 2001.
[5] 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
[6] W. H. Liao, “Region Description Using Extended Local Ternary Patterns”, Proceedings of the 20th International Conference on Pattern Recognition, pp. 1003-1006, 2010.
[7] Marko Heikkil¨a, Matti Pietik¨ainen, and Cordelia Schmid, “Description of Interest Regions with Center-Symmetric Local Binary Patterns”, Computer Vision, Graphics and Image Processing(CVPR `06), Volume 4338, pp 58-69, 2006.
[8] 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.
[9] A. Shobeirinejad and Y. S. Gao, “Gender Classification Using Interlaced Derivative Patterns“, Proceedings of the 20th International Conference on Pattern Recognition, pp. 1509-1512, 2010.
[10] M. Heikkilä, M. Pietikäinen and C. Schmid, “Description of Interest Regions with Center-Symmetric Local Binary Patterns”, Computer Vision, Graphics and Image Processing, Lecture Notes in Computer Science, 2006.
[11] J. C. Dunn "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics 3: 32-57, 1973.
[12] Timo Ojala, Topi Mäenpää, Matti Pietikäinen, Jaakko Viertola, Juha Kyllönen and Sami Huovinen, “Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms”, 21st International Conference on Pattern Recognition, November 11-15, 2012.
[13] Alexander Strehl and Joydeep Ghosh, “Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions”, Journal of Machine Learning Research 3, 583-617,2002.
[14] Ulrike von Luxburg, “A Tutorial on Spectral Clustering”, Statistics and Computing, 17 (4), 2007.
[15] Wen-Hung Liao, “Commensurate dimensionality reduction for extended local ternary patterns”, International Conference on Pattern Recognition (ICPR), 2012.
[16] Xiaosheng Wu and Junding Sun, “An Extended Center-Symmetric Local Ternary Patterns for Image Retrieval”, International Conference, CSEE 2011.		zh_TW