Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/32594
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dc.contributor.advisor吳柏林zh_TW
dc.contributor.advisorWu, Ber Linen_US
dc.contributor.author黃瑞華zh_TW
dc.contributor.authorHuang, Jui Huaen_US
dc.creator黃瑞華zh_TW
dc.creatorHuang, Jui Huaen_US
dc.date2007en_US
dc.date.accessioned2009-09-17T05:48:52Z-
dc.date.available2009-09-17T05:48:52Z-
dc.date.issued2009-09-17T05:48:52Z-
dc.identifierG0094972003en_US
dc.identifier.urihttps://nccur.lib.nccu.edu.tw/handle/140.119/32594-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description應用數學研究所zh_TW
dc.description94972003zh_TW
dc.description96zh_TW
dc.description.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。\n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。\n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.abstractThis 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.” \nAn 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. \nExperimental 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.tableofcontentsAbstract (in Chinese)\nAbstract (in English)\nAcknowledgements\nContents\nList of Tables\nList of Figures\n\nCHAPTER 1 INTRODUCTION\n1.1 Motivation of the research\n1.2 Survey of related researches\n1.3 Sketch of the research work\n1.4 Thesis organization\n\nCHAPTER 2 PRINCIPAL TYPES OF PHOTOGRAPHIC COMPOSITIONS\n2.1 Sun-like Composition (SC)\n2.2 Golden-section-like Composition (GC)\n2.3 Diagonal-like Composition (DC)\n2.4 Frame-like Composition (FC)\n2.5 Symmetry-like Composition (SMC)\n2.6 Triangle-like Composition (TC)\n2.7 Vanishing-point-like Composition (VC)\n2.8 Horizontal/vertical-line-like Composition (HVC)\n\nCHAPTER 3 FEATURES ANALYSIS AND EXTRACTION \n3.1 Feature Analysis\n3.2 Features extraction \n3.2.1 Feature 1: Sharpness\n3.2.2 Feature 2: Brightness average\n3.2.3 Feature 3~6: Local linearity\n3.2.4 Feature 7: Symmetry\n3.2.5 Feature 8~10: Global major line direction\n\nCHAPTER 4 FUZZY LOGIC FUSION\n4.1 Basic concepts\n4.2 Input variables\n4.3 Membership functions\n4.4 Fuzzy logic rule-based fusion\n\nCHAPTER 5 EXPERIMENTAL RESULTS\n5.1 The typical experiment\n5.2 The test sets\n\nCHAPTER 6 SUMMARY AND FUTURE RESEARCH \n6.1 Summary and conclusions\n6.2 Topics for future research\n\nREFERENCES \nList of Tables\n\nTable 1. The requirements of features extraction of 25 selected ROI’s. \nTable 2. True average values of 6 estimated features in 25 ROIs.\nTable 3. Confidences of 6 features in Table 2.\nTable 4. The fuzzy “High(H)” memberships of confidences in Table 3.\nTable 5. The memberships of 8 photographic compositions about the Fig. 6.\nTable 6.1. Sample images with sun-like composition.\nTable 6.2. Sample images with golden-like composition.\nTable 6.3. Sample images with diagonal-like composition.\nTable 6.4. Sample images with frame-like composition.\nTable 6.5. Sample images with symmetry-like composition.\nTable 6.6. Sample images with triangle-like composition.\nTable 6.7. Sample images with vanishing-point-like composition.\nTable 6.8. Sample images with Horizontal/Vertical-line-like composition.\nTable 7. The correlations between different photographic compositions and the outputs of fuzzy logic rules.\n \nList of Figures\nFig. 1. The sun-like photographic composition.\nFig. 2. The golden-section-like photographic composition.\nFig. 3. The diagonal-like photographic composition.\nFig. 4. The frame-like photographic composition.\nFig. 5. The symmetry-like photographic composition.\nFig. 6. The triangle-like photographic composition.\nFig. 7. The vanishing-point-like photographic composition.\nFig. 8. The horizontal/vertical-like photographic composition.\nFig. 9. The locations of 25 ROI’s.\nFig.10. Flowchart of the proposed algorithm for identifying the photographic composition based on fuzzy logic.\nFig. 11. Two membership functions for input variables.\nFig.12. The identifying system output membership functions.\nFig. 13 (a) Test image. (b) The 25 ROI images with size 17×17.\nFig. 14. The sharpness of 25 ROIs from the Fig. 13(b).\nFig. 15. The edge map of the tested image in Fig. 13(a).\nFig. 16. The four features of linearity of the tested image. The abscissa of the plots denotes the number of the ROI. \nFig.17. The symmetric feature of all ROI pairs. The color blocks denote the corresponded ROI pair is referenced in the recognition procedure.\nFig. 18. The Hough space of the tested image\nFig. 19. The histogram of the directions of straight lines in the tezh_TW
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dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0094972003en_US
dc.subject模糊邏輯zh_TW
dc.subject影像特徵zh_TW
dc.subject圖形辨識zh_TW
dc.subject攝影構圖zh_TW
dc.subjectFuzzy logicen_US
dc.subjectImage featuresen_US
dc.subjectPattern recognitionen_US
dc.subjectPhotographic compositionen_US
dc.title應用模糊邏輯的攝影構圖辨認方法zh_TW
dc.titleA Fuzzy Logic Approach for Recognition of Photographic Compositionsen_US
dc.typethesisen
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