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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/63711
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/63711


    Title: 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
    Authors: 劉嘉瑜
    Contributors: 廖文宏
    劉嘉瑜
    Keywords: 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
    Date: 2013
    Issue Date: 2014-02-10 14:57:07 (UTC+8)
    Abstract: 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.
    Reference: [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.
    Description: 碩士
    國立政治大學
    資訊科學學系
    100753015
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100753015
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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