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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/78754


    Title: 基於點群排序關係的特徵描述子建構
    Feature descriptor based on local intensity order relations of pixel group
    Authors: 吳家禎
    Wu, Chia Chen
    Contributors: 廖文宏
    吳家禎
    Wu,Chia Chen
    Keywords: 影像特徵描述子
    點群排序關係
    影像比對
    feature descriptor
    local intensity order relations
    image recognition
    Date: 2015
    Issue Date: 2015-10-01 14:18:03 (UTC+8)
    Abstract: 隨著科技的進步以及網際網路的普及，影像資訊的傳遞已經漸漸取代文字的表達，人們對於影像的需求也越來越多元，使得影像處理技術以及影像資訊分析也就越來越重要。然而，影像中其中一項重要的資訊為特徵描述子，強而有力的描述子能使得影像在辨識、分類等應用上有較佳的回饋，描述子的建構方式根據編碼原則分為：基於區域梯度統計、基於點對關係以及基於點群關係。其中，基於點群關係的編碼方式因為點群的選取及排序過程中，可能會產生過多的關係表示方法數，以至於不利於計算，因此過去較少有利用點群關係的編碼方式所建構而成的特徵描述子。
    本論文提出描述子建構方式-LIOR，是以點群排序關係為基礎的編碼方式，相較於LIOP方法隨著點群內的點數增加，元素關係數大幅度的成長，造成描述子維度過大，計算時間和空間皆可能需要大量的消耗，而本研究方法足以改善計算維度的問題，重新定義點群關係的排名機制，並以像素值為基準加入權重分配，以區別加權排序之間不同大小差值所造成的影響程度。
    實驗結果顯示本研究方法對於不同影像劣化效果的資料集，不僅能提升選取多點為一群的影像比對評估效能，同時也能改善點群內元素關係過多的排名表示法，降低以多點為群集的特徵描述子維度，節省了影像比對的計算時間以及空間，仍可維持整體影像配對之效能。
    Reference: [1] WANG, Zhenhua; FAN, Bin; WU, Fuchao. “Local intensity order pattern for feature description.” In: Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011. p. 603-610.
    [2] LOWE, David G. Object recognition from local scale-invariant features. In:Computer vision, 1999. The proceedings of the seventh IEEE international conference on. IEEE, 1999. p. 1150-1157.
    [3] BAY, Herbert; TUYTELAARS, Tinne; VAN GOOL, Luc. SURF: Speeded up robust features. In: Computer Vision–ECCV 2006. Springer Berlin Heidelberg, 2006. p. 404-417.
    [4] TOLA, Engin; LEPETIT, Vincent; FUA, Pascal. DAISY: An efficient dense descriptor applied to wide-baseline stereo. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2010, 32.5: 815-830.
    [5] CALONDER, Michael, et al. BRIEF: Binary robust independent elementary features. In: Computer Vision–ECCV 2010. Springer Berlin Heidelberg, 2010. p. 778-792.
    [6] LEUTENEGGER, Stefan; CHLI, Margarita; SIEGWART, Roland Yves. BRISK: Binary robust invariant scalable keypoints. In: Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011. p.2548-2555.
    [7] RUBLEE, Ethan, et al. ORB: an efficient alternative to SIFT or SURF. In:Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011. p. 2564-2571.
    [8] Ojala, Timo, Matti Pietikainen, and Topi Maenpaa. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.7 (2002): 971-987.
    [9] ALAHI, Alexandre; ORTIZ, Raphael; VANDERGHEYNST, Pierre. FREAK: Fast retina keypoint. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. Ieee, 2012. p. 510-517.
    [10] Kendall, Maurice George. "Rank correlation methods." (1948).
    [11] Miksik, O., Mikolajczyk, K.: Evaluation of local detectors and descriptors for fast feature matching, In Pattern Recognition (ICPR), 21st International Conference on pp. 2681~2684, 2012.
    Description: 碩士
    國立政治大學
    資訊科學學系
    102753010
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0102753010
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

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