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E-raamat: Computational Approaches in the Transfer of Aesthetic Values from Paintings to Photographs: Beyond Red, Green and Blue

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 18-Jul-2017
  • Kirjastus: Springer Verlag, Singapore
  • Keel: eng
  • ISBN-13: 9789811035616
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Computational Approaches in the Transfer of Aesthetic Values from Paintings to Photographs: Beyond Red, Green and BlueSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 18-Jul-2017
  • Kirjastus: Springer Verlag, Singapore
  • Keel: eng
  • ISBN-13: 9789811035616
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This book examines paintings using a computational and quantitative approach. Specifically, it compares paintings to photographs, addressing the strengths and limitations of both. Particular aesthetic practices are examined such as the vista, foreground to background organisation and the depth planes. These are analysed using a range of computational approaches and clear observations are made. New generations of image-capture devices such as Google goggles and the light field camera, promise a future in which the formal attributes of a photograph are made available for editing to a degree that has hitherto been the exclusive territory of painting. In this sense paintings and photographs are converging, and it therefore seems an opportune time to study the comparisons between them. In this context, the book includes cutting-edge work examining how some of the aesthetic attributes of a painting can be transferred to a photograph using the latest computational approaches.
Part I Image Attributes and Aesthetic Aspects
1 The Portrait and Landscape Genres
3(6)
1.1 The Portrait Genre
3(2)
1.2 The Landscape Genre
5(3)
1.3 Concluding Remarks
8(1)
References
8(1)
2 The Colour Attributes of Paintings
9(24)
2.1 Painters, Paint and Colour Systems
9(1)
2.2 Colour Spaces
10(6)
2.3 Colour Contrast
16(14)
2.3.1 Itten's Contrasts
17(1)
2.3.2 Structural Contrasts
18(7)
2.3.3 Hue and Harmony
25(5)
2.4 Concluding Remarks
30(3)
References
31(2)
3 Computational Models of Colour Contrast
33(6)
3.1 Contrast Models for Simple Images
33(1)
3.2 Contrast Models for Complex Images
34(2)
3.3 Contrast Models for Hue Contrast
36(3)
References
37(2)
4 The Geometric Attributes of Paintings
39(16)
4.1 Composition in Paintings
42(4)
4.1.1 The Rule of Thirds
42(2)
4.1.2 Region of Interest and the Horizon
44(2)
4.2 Vignetting
46(5)
4.3 Concluding Remarks
51(4)
References
51(4)
Part II Transfer of Aesthetic Values
5 Global Colour Style Transfer
55(16)
5.1 Global Hue, Saturation and Lightness Analysis
56(2)
5.2 Hue Mapping
58(4)
5.2.1 RGB to RYB Conversion
58(2)
5.2.2 Dominant Hue Extraction
60(1)
5.2.3 Dominant Hue Alignment and Mapping
61(1)
5.3 Saturation and Lightness Mapping
62(1)
5.4 Experiments and Discussion
62(8)
5.5 Concluding Remarks
70(1)
References
70(1)
6 Atmospheric Perspective Effect Transfer for Landscape Photographs
71(36)
6.1 Introduction
71(3)
6.2 Related Work
74(2)
6.3 Paintings Versus Photographs
76(2)
6.3.1 Depth Planes
76(1)
6.3.2 Contrast in Paintings and Photographs
76(2)
6.4 Contrast Manipulation
78(11)
6.4.1 Reference Selection
79(2)
6.4.2 Depth-Aware Contrast Mapping
81(8)
6.5 Experiments and Discussion
89(13)
6.6 Concluding Remarks
102(5)
References
104(3)
7 Regional Contrast Manipulation for Portrait Photograph Enhancement
107(30)
7.1 Introduction
107(3)
7.2 Related Work
110(1)
7.3 Paintings Versus Photographs
111(2)
7.4 Example-Based Portrait Photograph Enhancement
113(7)
7.4.1 Reference Selection
114(3)
7.4.2 Contrast Mapping
117(3)
7.5 Experiments and Discussion
120(13)
7.6 Concluding Remarks
133(4)
References
134(3)
8 Composition Improvement for Portrait Photographs
137(18)
8.1 Introduction
137(1)
8.2 Related Work
138(1)
8.3 Proposed Composition Improvement Method
139(5)
8.3.1 Graph Matching
141(2)
8.3.2 Space Cropping
143(1)
8.4 Experiments
144(8)
8.5 Concluding Remarks
152(3)
References
152(3)
9 Vignetting Effect Transfer
155(24)
9.1 Introduction
155(1)
9.2 Related Work
156(1)
9.3 Vignetting Effect in Paintings
157(4)
9.4 Transfer of Painter-Style Vignetting to Photographs
161(10)
9.4.1 Lightness Weighting Pattern and Blending Function
161(2)
9.4.2 Lightness Weighting Pattern Correction
163(3)
9.4.3 Content-Aware Interpolation
166(4)
9.4.4 Local Contrast Restoration
170(1)
9.5 Experiments and Discussion
171(5)
9.6 Concluding Remarks
176(3)
References
178(1)
10 Defining Hue Contrast
179(12)
10.1 Introduction
179(1)
10.2 Related Work
180(1)
10.3 Defining Hue Contrast
180(1)
10.4 Our Approach
181(2)
10.5 Computational Details
183(1)
10.6 Results and Interpretations
184(4)
10.7 Evaluation Through a User Study
188(1)
10.8 Concluding Remarks
189(2)
References
189(2)
11 Interactive Local Hue Contrast Manipulation
191
11.1 Introduction
191(3)
11.2 Related Work
194(1)
11.3 Hue Manipulation Framework
195(5)
11.3.1 System Overview
195(2)
11.3.2 Segmentation
197(1)
11.3.3 Hue Histogram Selection
197(2)
11.3.4 Hue Contrast Manipulation Operators
199(1)
11.4 Experimental Results
200(3)
11.4.1 Hue Compression and Hue Stretch
200(2)
11.4.2 Combining the Three Operations
202(1)
11.5 Discussion and User Feedback
203(1)
11.6 Concluding Remarks
204
References
204
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