1. NCCU Academic Hub
  2. 學術產出
  3. College of Science
  4. Theses
  5. Item

學術產出-Theses

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

  • No doi shows Citation Infomation
題名 應用模糊邏輯的攝影構圖辨認方法
A Fuzzy Logic Approach for Recognition of Photographic Compositions
作者 黃瑞華
Huang, Jui Hua
貢獻者 吳柏林
Wu, Ber Lin
黃瑞華
Huang, Jui Hua
關鍵詞 模糊邏輯
影像特徵
圖形辨識
攝影構圖
Fuzzy logic
Image features
Pattern recognition
Photographic composition
日期 2007
上傳時間 17-Sep-2009 13:48:52 (UTC+8)
摘要 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.
參考文獻 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.
描述 碩士
國立政治大學
應用數學研究所
94972003
96
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094972003
資料類型 thesis
dc.contributor.advisor 吳柏林zh_TW
dc.contributor.advisor Wu, Ber Linen_US
dc.contributor.author (Authors) 黃瑞華zh_TW
dc.contributor.author (Authors) Huang, Jui Huaen_US
dc.creator (作者) 黃瑞華zh_TW
dc.creator (作者) Huang, Jui Huaen_US
dc.date (日期) 2007en_US
dc.date.accessioned 17-Sep-2009 13:48:52 (UTC+8)-
dc.date.available 17-Sep-2009 13:48:52 (UTC+8)-
dc.date.issued (上傳時間) 17-Sep-2009 13:48:52 (UTC+8)-
dc.identifier (Other Identifiers) G0094972003en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/32594-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學研究所zh_TW
dc.description (描述) 94972003zh_TW
dc.description (描述) 96zh_TW
dc.description.abstract (摘要) 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。		zh_TW
dc.description.abstract (摘要) 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.		en_US
dc.description.tableofcontents Abstract (in Chinese)
Abstract (in English)
Acknowledgements
Contents
List of Tables
List of Figures

CHAPTER 1 INTRODUCTION
1.1 Motivation of the research
1.2 Survey of related researches
1.3 Sketch of the research work
1.4 Thesis organization

CHAPTER 2 PRINCIPAL TYPES OF PHOTOGRAPHIC COMPOSITIONS
2.1 Sun-like Composition (SC)
2.2 Golden-section-like Composition (GC)
2.3 Diagonal-like Composition (DC)
2.4 Frame-like Composition (FC)
2.5 Symmetry-like Composition (SMC)
2.6 Triangle-like Composition (TC)
2.7 Vanishing-point-like Composition (VC)
2.8 Horizontal/vertical-line-like Composition (HVC)

CHAPTER 3 FEATURES ANALYSIS AND EXTRACTION
3.1 Feature Analysis
3.2 Features extraction
3.2.1 Feature 1: Sharpness
3.2.2 Feature 2: Brightness average
3.2.3 Feature 3~6: Local linearity
3.2.4 Feature 7: Symmetry
3.2.5 Feature 8~10: Global major line direction

CHAPTER 4 FUZZY LOGIC FUSION
4.1 Basic concepts
4.2 Input variables
4.3 Membership functions
4.4 Fuzzy logic rule-based fusion

CHAPTER 5 EXPERIMENTAL RESULTS
5.1 The typical experiment
5.2 The test sets

CHAPTER 6 SUMMARY AND FUTURE RESEARCH
6.1 Summary and conclusions
6.2 Topics for future research

REFERENCES
List of Tables

Table 1. The requirements of features extraction of 25 selected ROI’s.
Table 2. True average values of 6 estimated features in 25 ROIs.
Table 3. Confidences of 6 features in Table 2.
Table 4. The fuzzy “High(H)” memberships of confidences in Table 3.
Table 5. The memberships of 8 photographic compositions about the Fig. 6.
Table 6.1. Sample images with sun-like composition.
Table 6.2. Sample images with golden-like composition.
Table 6.3. Sample images with diagonal-like composition.
Table 6.4. Sample images with frame-like composition.
Table 6.5. Sample images with symmetry-like composition.
Table 6.6. Sample images with triangle-like composition.
Table 6.7. Sample images with vanishing-point-like composition.
Table 6.8. Sample images with Horizontal/Vertical-line-like composition.
Table 7. The correlations between different photographic compositions and the outputs of fuzzy logic rules.

List of Figures
Fig. 1. The sun-like photographic composition.
Fig. 2. The golden-section-like photographic composition.
Fig. 3. The diagonal-like photographic composition.
Fig. 4. The frame-like photographic composition.
Fig. 5. The symmetry-like photographic composition.
Fig. 6. The triangle-like photographic composition.
Fig. 7. The vanishing-point-like photographic composition.
Fig. 8. The horizontal/vertical-like photographic composition.
Fig. 9. The locations of 25 ROI’s.
Fig.10. Flowchart of the proposed algorithm for identifying the photographic composition based on fuzzy logic.
Fig. 11. Two membership functions for input variables.
Fig.12. The identifying system output membership functions.
Fig. 13 (a) Test image. (b) The 25 ROI images with size 17×17.
Fig. 14. The sharpness of 25 ROIs from the Fig. 13(b).
Fig. 15. The edge map of the tested image in Fig. 13(a).
Fig. 16. The four features of linearity of the tested image. The abscissa of the plots denotes the number of the ROI.
Fig.17. The symmetric feature of all ROI pairs. The color blocks denote the corresponded ROI pair is referenced in the recognition procedure.
Fig. 18. The Hough space of the tested image
Fig. 19. The histogram of the directions of straight lines in the te		zh_TW
dc.format.extent 50376 bytes-
dc.format.extent 72603 bytes-
dc.format.extent 73666 bytes-
dc.format.extent 26198 bytes-
dc.format.extent 23014 bytes-
dc.format.extent 93044 bytes-
dc.format.extent 68432 bytes-
dc.format.extent 69474 bytes-
dc.format.extent 584681 bytes-
dc.format.extent 18301 bytes-
dc.format.extent 49757 bytes-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094972003en_US
dc.subject (關鍵詞) 模糊邏輯zh_TW
dc.subject (關鍵詞) 影像特徵zh_TW
dc.subject (關鍵詞) 圖形辨識zh_TW
dc.subject (關鍵詞) 攝影構圖zh_TW
dc.subject (關鍵詞) Fuzzy logicen_US
dc.subject (關鍵詞) Image featuresen_US
dc.subject (關鍵詞) Pattern recognitionen_US
dc.subject (關鍵詞) Photographic compositionen_US
dc.title (題名) 應用模糊邏輯的攝影構圖辨認方法zh_TW
dc.title (題名) A Fuzzy Logic Approach for Recognition of Photographic Compositionsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. T. S. Huang, Travel with a camera, Chen Chung Book Compony, Jan. 2003.zh_TW
dc.relation.reference (參考文獻) 2. A. McAndrew, Introduction to digital image processing with Matlab, Thomson Learning Inc., 2004.zh_TW
dc.relation.reference (參考文獻) 3. R. C. Gonzalez and R. E. Woods, Digital image processing, Addison-Wesley, 1992.zh_TW
dc.relation.reference (參考文獻) 4. L. G. Shapiro and G. C. Stockman, Computer vision, NJ: Prentice-Hall, 2001, pp. 304-312.zh_TW
dc.relation.reference (參考文獻) 5. H. D. Cheng and H. Xu, “A novel fuzzy logic approach to contrast enhancement,” Pattern Recognition, vol. 33, 2000, pp. 809-819.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 9. G. Klir and B. Yuan, Fuzzy sets and fuzzy logic: theory and applications, Englewood Cliffs, NJ: Prentice-Hall, 1995.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 11. S. K. Pal and A. Ghosh, Soft computing approach to pattern recognition and image processing, World Scientific, 2002.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 13. M. Sugeno, and G.T. Kang, “Structure Identification of fuzzy model,” Fuzzy Sets and Systems, vol. 28, 1988, pp. 15-33.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 16. 施威銘研究室, 數位相機的實拍解析, Flag Publishing, Feb. 2006.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW