政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32594
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113393/144380 (79%)
Visitors : 51240971      Online Users : 909
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/32594


    Title: 應用模糊邏輯的攝影構圖辨認方法
    A Fuzzy Logic Approach for Recognition of Photographic Compositions
    Authors: 黃瑞華
    Huang, Jui Hua
    Contributors: 吳柏林
    Wu, Ber Lin
    黃瑞華
    Huang, Jui Hua
    Keywords: 模糊邏輯
    影像特徵
    圖形辨識
    攝影構圖
    Fuzzy logic
    Image features
    Pattern recognition
    Photographic composition
    Date: 2007
    Issue Date: 2009-09-17 13:48:52 (UTC+8)
    Abstract: 本論文應用攝影構圖法則，以模糊邏輯理論為基礎，判別影像的攝影構圖類型。構圖(Composition)乃是攝影這項平面藝術創作最重要的美學元素之一，其目的是利用空間中的物體配置，經由透視投影後，讓畫面的整體呈現平衡感；專業的、優秀的攝影作品，皆會符合攝影的基本構圖原理。因此許多的影像增強、影像合成的應用中，也應該配合相片原本的構圖設計，針對所欲表達的重點予與適當地調整，而非「盲目的」以一體適用的法則去處理每一張照片。
    論文中，我們針對影像所欲表達的重點區域，分析其結構特性，設計不同的特徵，並以模糊邏輯理論為基礎，應用Mamdani系統，結合隸屬函數與攝影構圖判別法則的交互作用，用以辨認所欲處理相片的構圖類別。依據辨認後的構圖類別，即可對該影像做適當地分割及調整，以使相片能有最佳的影像增強處理。
    實驗證明，本文所提出的方法能有效地辨認攝影構圖類別，針對不同攝影構圖所作的影像修正，才能更符合人眼的視覺喜好。
    This thesis addresses the problem of how to recognize the photographic composition from a given photo based on the theory of fuzzy logic. Composition is one of the important aesthetics for the plane figure photo art. To present the balance of its holistic picture, it takes the advantage of special object arrangement after acting perspective projection. A piece of professional and qualified photo work will realize these basic photo composition methods. For many applications about the digital photo, the operations, i.e., photo enhancement, segmentation, output, and synthesis, all need to match up the photographic composition to do accurate processing rather than “blind” processing that assumes each photo with the same “composition.”
    An automatic recognition method using image features from some specific regions is described. The method is employed in a Mamdani model and combines outputs of multiple fuzzy logic rules and feature extraction algorithms to obtain confidences that can identify the correct photographic composition.
    Experimental results show that the proposed method is robust and effective for photographic composition recognition. The feature with adjusting in different photo composing will be able to comfort our human sight.
    Reference: 1. T. S. Huang, Travel with a camera, Chen Chung Book Compony, Jan. 2003.
    2. A. McAndrew, Introduction to digital image processing with Matlab, Thomson Learning Inc., 2004.
    3. R. C. Gonzalez and R. E. Woods, Digital image processing, Addison-Wesley, 1992.
    4. L. G. Shapiro and G. C. Stockman, Computer vision, NJ: Prentice-Hall, 2001, pp. 304-312.
    5. H. D. Cheng and H. Xu, “A novel fuzzy logic approach to contrast enhancement,” Pattern Recognition, vol. 33, 2000, pp. 809-819.
    6. H. M. Zhang, L. Q. Han, and Z. Wang, “A fuzzy classification system and its application,” in Proceedings of the 2nd International Conference on Machine Learning and Cybernetics, 2-5 Nov. 2003, pp. 2582-2586.
    7. A. K. Jain, R. P. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, 2000, pp. 4-37.
    8. Y. Suzuki, K. I. Itakura, S. Saga, and J. Maeda, “Signal processing and pattern recognition with soft computing,” Proceedings of the IEEE, vol. 89, no. 9, Sept. 2001, pp. 1297-1317.
    9. G. Klir and B. Yuan, Fuzzy sets and fuzzy logic: theory and applications, Englewood Cliffs, NJ: Prentice-Hall, 1995.
    10. Y. Chen, M. Shen, and Y. He, “A method of pattern recognition based upon synthetic technology of fuzzy logic and neural network,” in Proceedings of 1993 IEEE Region 10 Conference on Computer, Communication, Control and Power Engineering, vol. 2, Beijing, China, 19-21 Oct 1993, pp.815-818.
    11. S. K. Pal and A. Ghosh, Soft computing approach to pattern recognition and image processing, World Scientific, 2002.
    12. E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” International Journal of Man-Machine Studies, vol. 21, 1975, pp. 213-227.
    13. M. Sugeno, and G.T. Kang, “Structure Identification of fuzzy model,” Fuzzy Sets and Systems, vol. 28, 1988, pp. 15-33.
    14. T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modelling and control,” IEEE Trans. On Systems, Man and Cybernetics, vol. 15, 1985, pp. 116-132.
    15. P. Manley-Cooke and M. Razas, “A modified fuzzy inference system for pattern classification,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), vol. 1, 23-26 Aug. 2004, pp. 256-259.
    16. 施威銘研究室, 數位相機的實拍解析, Flag Publishing, Feb. 2006.
    17. S. Banerjee and B. L. Evans, “Unsupervised automation of photographic composition rules in digital still cameras,” in Proceeding SPIE Conference on Sensors, Color, Cameras, and Systems for Digital Photography, Jan. 2004.
    18. J. R. Smith and S. F. Chang, “Tools and techniques for color image retrieval,” in SPIE Proceeding of Symposium on Electronic Imaging: Science and Technology, vol. 2670, San Jose CA., Feb. 1996.
    19. P. D. Gader, B. N. Nelson, H. Frigui, G. Vaillette, and J. M. Keller, “Fuzzy logic detection of landmines with ground penetrating radar,” Signal Processing, vol. 80, 2000, pp. 1069-1084.
    20. P. R. Kersten, “The fuzzy median and the fuzzy MAD,” in Proceedings of ISUMA - NAFIPS `95 The Third International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, 17-20 Sept. 1995, pp. 85-88.
    21. P. D. Gader, J. M. Keller, and B. N. Nelson, “Recognition technology for the detection of buried land mines,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 1, Feb. 2001, pp. 31-43.
    Description: 碩士
    國立政治大學
    應用數學研究所
    94972003
    96
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0094972003
    Data Type: thesis
    Appears in Collections:[Department of Mathematical Sciences] Theses

    Files in This Item:

    File Description SizeFormat
    200301.pdf49KbAdobe PDF2844View/Open
    200302.pdf70KbAdobe PDF21518View/Open
    200303.pdf71KbAdobe PDF2874View/Open
    200304.pdf25KbAdobe PDF2752View/Open
    200305.pdf22KbAdobe PDF2899View/Open
    200306.pdf90KbAdobe PDF21211View/Open
    200307.pdf66KbAdobe PDF2847View/Open
    200308.pdf67KbAdobe PDF2892View/Open
    200309.pdf570KbAdobe PDF2793View/Open
    200310.pdf17KbAdobe PDF2704View/Open
    200311.pdf48KbAdobe PDF2870View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback