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研究生:王星翰
研究生(外文):Hsing-Han,Wang
論文名稱:影片轉漫畫:將動畫影片轉換為漫畫
論文名稱(外文):Animation2Comics: Transforming Animation Videos into Comics
指導教授:朱威達
指導教授(外文):Wei-Ta Chu
口試委員:郭景明施皇嘉王昱舜
口試日期:2012-07-09
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:65
中文關鍵詞:動畫轉漫畫基因演算法最佳化頁面配置版面選擇動畫影片漫畫
外文關鍵詞:Animation to comicsgenetic algorithmoptimized page allocationlayout selectionanimationscomics
相關次數:
  • 被引用被引用:1
  • 點閱點閱:794
  • 評分評分:
  • 下載下載:138
  • 收藏至我的研究室書目清單書目收藏:1
隨著動畫技術以及市場的成熟及進步,全世界每年出產許多的動畫影片。根據日本動畫協會的統計,從2004年起,日本每年都產出至少200部的動畫影片。對於動漫迷來說,每天或每星期他們迫不及待的希望趕快看到每部新出品的影片內容。因此,如何有效瀏覽大量的動畫成為一個重要的問題,在本論文我們提出一套可以將動畫轉換為漫畫的系統,除了可以加快瀏覽的速度之外,同時增加了瀏覽的樂趣。我們首先偵測鏡頭切換,並為每個鏡頭選擇一些畫面來表示,接著進行場景偵測。針對個別的場景,我們設計一個最佳化架構來安排那些畫面要放到同一漫畫的頁面中。配置的方法有許多可能性,且每個畫面都是必須連續的,所以在本論文我們採用基因演算法,經由演化的過程得到一個最好的配置。得到了安排的頁面中有哪些畫面後,接著是安排這些畫面要怎樣排版。首先我們設計出許多版面,並且經由計算畫面與版面的相似性,選擇最好的版面。選完版面後,我們在畫面中選取適當的區域,然後將畫面縮放貼到對應的畫格。最後我們就可以得到由原始動畫影片產生的漫畫。
在實驗中我們計算場景偵測的精準度、基因演算法的遞迴狀況,以及進行使用者的主觀實驗。在此我們以轉換後的漫畫,原始的動畫影片以及由商業軟體KMPlayer所產生的預覽圖做比較,根據使用者的回饋,大部分的使用者肯定我們的效果並且展現高度的興趣。
With the advances of animation technology and industries, many animations are produced every year. According to the latest statistics released by the Association of Japanese Animations, more than 200 animations produced every year from 2004 only in Japan. For animation fans, they keep their eyes on the newly-released episodes every day or every week. How to efficiently browse large volume of animation, therefore, becomes an important problem. In this thesis, we propose a media transforming system that transforms animations into comics. This system can not only increase browsing speed but also increase browsing experience. We first conduct shot change detection, select keyframes from each shot, and conduct scene detection. We devise a method to arrange frames into pages, and then select the most appropriate layout for presentation. We select appropriate region of each keyframe to stick on the targeted cell of the layout. Finally, we can generate comics that appropriately display information of the original animation.
In experiments, we show performance of scene boundary detection and the iteration process of the optimization framework. In addition, we design a user study to evaluate how users feel about the generated comic pages. The user study show very promising results.
TABLE OF CONTENTS
摘要............................................................... i
Abstract ......................................................... ii
TABLE OF CONTENTS ................................................ iii
LIST OF FIGURES .................................................. v
LIST OF TABLES ................................................... vii
Chapter 1 INTRODUCTION............................................ 1
1.1 Motivation ................................................... 1
1.2 Challenges ................................................... 3
1.3 Contributions................................................. 4
1.4 Thesis Organization .......................................... 5
Chapter 2 RELATED WORKS .......................................... 6
2.1 Video Summarization .......................................... 6
2.1.1 Static video abstract ...................................... 7
2.1.2 Dynamic video skimming ..................................... 7
2.2 Media Representation Transformation .......................... 8
2.3 Summary ...................................................... 10
Chapter 3 ANIMATION2COMICS ....................................... 11
3.1 System Framework ............................................. 11
3.2 Shot Change Detection ........................................ 12
3.3 Keyframe Selection ........................................... 17
3.4 Scene Boundary Detection ..................................... 18
3.5 Optimized Page Allocation .................................... 22
3.5.1 Introduction to Genetic Algorithms (GA)..................... 23
3.5.2 Main Components of GA ...................................... 24
Chromosome Representation and Initial Population ................. 24
Objective Function ............................................... 25
Selection Operation .............................................. 26
Crossover Operation .............................................. 27
Mutation Operation ............................................... 28
3.5.3 GA Process Summary ......................................... 28
3.6 Page Layout Selection......................................... 30
3.6.1 Layout Design .............................................. 31
3.6.2 Layout Selection ........................................... 31
3.7 Composition .................................................. 34
3.8 Summary ...................................................... 35
Chapter 4 EXPERIMENTAL RESULTS ................................... 36
4.1 Dataset and Experiment Setup ................................. 36
4.2 Video Scene Detection Performance ............................ 37
4.3 GA Iteration ................................................. 40
4.4 User Study ................................................... 42
4.4 Summary ...................................................... 49
Chapter 5 CONCLUSION AND FUTURE WORK ............................. 50
5.1 Conclusion ................................................... 50
5.2 Future Work .................................................. 50
Reference ........................................................ 52
APPENDIX ......................................................... 56
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