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系統識別號 U0002-1408201314472300
中文論文名稱 影像特效轉換系統
英文論文名稱 An image effect converter system
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 101
學期 2
出版年 102
研究生中文姓名 白逸群
研究生英文姓名 Yi-Chun Pai
學號 897410162
學位類別 博士
語文別 英文
口試日期 2013-07-04
論文頁數 41頁
口試委員 指導教授-林慧珍
委員-謝錦棠
委員-廖宏源
委員-顏淑惠
委員-施國琛
委員-林慧珍
中文關鍵字 影像抽象化  雙向濾波器  高斯濾波器  卡通化  非寫實渲染  shock filter  Kuwahara filter 
英文關鍵字 image abstraction  bilateral filter  Gaussian blur filter  cartoon animation  non-photorealistic rendering  shock filter  Kuwahara filter 
學科別分類 學科別應用科學資訊工程
中文摘要 在本論文中,我們提出了一個能夠有效的影像特效轉換系統,並能夠滿足不同使用者的設計需求。為了完成這樣的系統,我們會先介紹基礎影像處理效果,接著說明本系統如何產生出各種不同的影像特效。在實驗結果的部分,展現出了許多不同影像特效的結果。
英文摘要 This dissertation proposes an image effect converter system that can create various types of effects for images, to satisfy different needs of users for specific design goals. Some operations needed for creating image effects are first proposed, and various image effects that can be converted by the proposed system are illustrated. The experiments show very rich results in terms of image effects.
論文目次 Table of Contents
Table of Contents III
List of Figures IV
Chapter 1 Introduction 1
Chapter 2 Basic operators 4
2.1 Difference of Gaussian (DoG) 4
2.2 Kuwahara Filter 5
2.3 Bilateral Filter 5
2.4 Optional Color Quantization (OCQ) 6
2.5 Line segment filter 6
2.6 Tone adjustment 7
2.7 Shock Filter 9
2.8 Continuous Glass Pattern 10
Chapter 3 The proposed operations 13
3.1 Flow-based Bilateral Filter 13
3.2 Flow-based Gaussian filter 16
3.3 Curve-shaped filters 18
3.4 Line drawing 19
3.5 Pencil texture generator 21
3.6 Modified shock filter 22
Chapter 4 Proposed image effect creation methods 24
4.1 Image abstraction 24
4.2 Pencil sketch 26
4.3 Watercolor painting 27
4.4 Artistic imaging with Continuous Glass Pattern 28
Chapter 5 Experimental results 31
Chapter 6 Conclusion and future works 38
References 39

List of Figures
Figure 1. Examples of the line segment filters with different radians (a). θ0 = 0, (b). θ1 = π/8, (c). θ2 = π/4, (d). θ4 = π/2. 7
Figure 2. Tone distributions in a natural image and in a pencil drawing [24]. 7
Figure 3. An example of tone adjustment (a). original image, (b). histogram, (c). distribution Bright, (d). distribution Mild, (e). distribution Dark, (f). combination of (c)~(e) with ratio 5:2:1, (g). result. 8
Figure 4. Examples of CESF [30] (a). original images, (b). CESF (σ = 2, t = 10), (c) CESF (σ = 4, t = 10). 10
Figure 5. An example of Glass pattern [6] (a). vector field ((y2-1)+(1/3)xy, (1/3)(y2-1)-xy)), (b). the trajectories solving the corresponding differential equation, (c). a corresponding GP. 10
Figure 6. Continuous Glass Pattern 11
Figure 7. An example of CGP (a). original image, (b). synthetic painterly texture, (c). resulting painterly CGP image. 12
Figure 8. Two versions of bilateral filter, where the red dots denote pixel x, and the blue lines/curves and black line/curve are the traced paths along gradient direction/flow and tangent direction/flow, respectively. (a) orientation-aligned bilateral filter, (b) flow-based bilateral filter 13
Figure 9. Comparison of OABF and FBBF (a). original image, (b). result of OABF, (c). result of FBBF, (d~f) close-ups of (a~c). 15
Figure 10. Neighbors collected by (a) directional Gaussian filter (b) flow-based Gaussian filter 16
Figure 11 (a). original image, (b). result of directional Gaussian filtering (c). result of flow-based Gaussian filtering 17
Figure 12. Comparison of traditional Gaussian filter and the flow-based Gaussian filter (a). original image (b). traditional Gaussian filter (σ = 2) (c). flow-based Gaussian filter (σg = 0.33, σt = 9) 17
Figure 13. Comparisons of the original Gaussian filter and the flow-based Gaussian filter under different parameter settings (a). original image, (b). result of traditional Gaussian filter with σ = 1, (c).~(h). results of flow-based Gaussian filter with (c). σg = 0.33, σt = 3, (d). σg = 1, σt = 3, (e). σg = 0.33, σt = 5, (f). σg = 1, σt = 5, (g). σg = 0.33, σt = 9, (h). σg = 1, σt = 9. 18
Figure 14. Filters in horizontal direction (a). line segment filter, (b). concave-up curve-shaped filter, (c). concave-down curve-shaped filter. 19
Figure 15. Comparison of line drawings based on different filters (a). original image, (b). C. Lu et al., (c). LDrawing 1, (d). LDrawing 2, (e). LDrawing 3, (f).~(j). close-ups for (a)~(e). 20
Figure 16. An example of LDrawing 3 (a). original image, (b). result. 21
Figure 17. Examples of generated pencil texture (a). θ = 0, (b). θ = π/4, (c). combination of (a) and (b). 22
Figure 18. Transparency adjustment for texture image given in Fig. 16(c) with (a). α = 0. 25, (b). α = 0.5, (c). α = 0.75 22
Figure 19. An example of modified shock filter (a). original image, (b) result. 23
Figure 20. An image pyramid 24
Figure 21. Image pyramid processing 25
Figure 22. Results of image pyramid processing (a). original image, (b)~(d). results of 1, 2, and 3 iterations of pyramid processing, respectively. 25
Figure 23. Image abstraction (a). result of optional color quantization for Fig. 20(d), (b). result of adding edges for Fig. 21(a). 26
Figure 24. Comparison of three versions of pencil sketch (a). original image, (b).~(d). results of LDrawing 1, LDrawing 2, and LDrawing 3, respectively, (e).~(g). pencil sketches based on LDrawing 1, LDrawing 2, and LDrawing 3, respectively, (h). a modified version of (g). 27
Figure 25. An example of effect of watercolor painting 28
Figure 26. Results of CGP (a). original image, (b). abstraction result of (a), (c).&(d). vector fields of (a)&(b), (e).&(f). CGP results of (a)&(b). 29
Figure 27. Example of modified CGP (a). CGP result given in Fig. 7(a), (b). the flow-based Gaussian blurring result of (a), (c). shock filtering result of (b). 30
Figure 28. Example of modified CGP (a). original image, (b). CGP result of (a), (c). the flow-based Gaussian blurring result of (b), (d). shock filtering result of (c). 30
Figure 29. Results of abstraction (a).&(b). original images, (c).&(d). 3-iteration pyramid processing results of (a)&(b), (e).&(f). OCQ results of (c)&(d), (g).&(h). edge adding results of (e)&(f). 31
Figure 30. Examples of pencil sketch (a).&(e). original images, (b).&(f). pencil sketch using LDrawing 1, (c).&(g). pencil sketch using LDrawing 2, (d).&(h). pencil sketch using LDrawing 3. 32
Figure 31. Example of the color pencil sketch (a). original image, (b). result. 33
Figure 32. Results of watercolor painting (a). & (c). original images, (b). & (d). results of (a) and (c). 33
Figure 33. Results of some other effects (a).&(d). original images, (b).&(f). flow-based Gaussian filtering, (c).&(g). shock filtering (Gaussian σ = 1.2, 5 iterations), (d).&(h). flow-based Gaussian filtering + shock filtering 34
Figure 34. Examples of CGP (a).&(c). original images, (b).&(d). results. 34
Figure 35. Example of modified CGP (a).&(e).&(i). original images, (b).&(f).&(j). CGP (b).&(f).&(j). CGP + the flow-based Gaussian filtering, (d).&(h).&(l). CGP + the flow-based Gaussian filtering + shock filtering. 35
Figure 36. Examples of more effects (a). &(d).&(g).&(j). original images, (b).&(e).&(h).&(k). shock filtering, (c).&(f).&(i).&(l). modified shock filtering + flow-based Gaussian filtering. 36
Figure 37. The interface of the proposed system 37
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