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研究生: 劉心如
Liu, Hsin-Ju
論文名稱: 運用標籤賽局於數位影像之主體重新對焦
Refocusing on the Object by a Labeling Game for Digital Images
指導教授: 張隆紋
Chang, Long-Wen
口試委員: 陳祝嵩
廖弘源
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊系統與應用研究所
Institute of Information Systems and Applications
論文出版年: 2012
畢業學年度: 100
語文別: 英文
論文頁數: 33
中文關鍵詞: 對焦影像分割標籤
外文關鍵詞: focusing, segmentation, labeling
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  • 在攝影取像時,場景中通常會存在多重目標物,對相機而言,這些目標物位於不同的深度上,攝影者選擇一個深度作為對焦的依據,用景深來呈現的想要的情境。而一張相片所呈現的視覺品質,受限於相機鏡頭的種類與拍攝者的攝影技術。若拍攝時對焦不準確,便無法呈現出拍攝者的意念及感覺,導致它成為一張失敗的影像。為了解決這個問題,本文提出一個概念,讓攝影者在拍攝時不用考慮對焦的問題,只需隨意拍攝一張全對焦影像,再透過影像處理事後對焦至想要的目標或區域即可。
    利用有限的影像資訊,我們提出一個以物件為基礎事後對焦架構,透過一個標籤賽局,來辨認出想要重新對焦的物件,透過空間濾波器使之在圖像中更為突出,並使背景模糊化模擬失焦的現象,進而達到景深設計的效果,提升整體圖像的視覺傳達品質。


    Generally there are more than one object that locate at different depth in a scene of a photographic image. Photographers select a depth as the basis of focusing to present the desired scenarios. However, the visual quality of a photo is limited by the type of camera lens and the skills of the photographer. It cannot present the photographer’s feeling and lead to a failure if we cannot focus on the wanted object properly. To solve this problem, this thesis proposes a concept that photographers don’t need to consider the focusing problem when shooting but refocus on it after computer processing.
    With limited image information, we propose an object-based refocusing framework to refocus on the wanted object. Through a image labeling game algorithm, we correctly identify the object which we want to refocus and make it more prominent in the image by using a spatial filter. Similarly, we simulate the out-of-focus effect by blurring the background. After these processes, we retrieve a correct focused image that can present the desired depth of field and improve the quality of visual communication.

    Abstract..........................2 Chapter 1 Introduction............1 Chapter 2 Related Work............3 2.1 Focusing......................3 2.2.1 Depth of field..............4 2.2.2 Focus technology............5 2.2 Segmentation..................7 2.3 Image Labeling Game...........9 Chapter 3 The Proposed Method....10 Chapter 4 Experiment Results.....23 Chapter 5 Conclusion.............31 References.......................32

    [1] Joe Demers. (2004) Depth of Field: A Survey of Techniques. [Online]. Available: http://http.developer.nvidia.com/

    [2] Glenn M. Cope. Depth of Field: The Misunderstood Element in Image Design. [Online] Available: http://www.photoclasses.com/

    [3] R. NG., M. Levoy, M. Brédif, G. Duval, M. Horowitz, and P. Hanrahan, “Light field photography with a hand-held plenoptic Camera”. Stanford Tech Report CTSR , 2005.

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    [7] Boykov, Y., and Jolly, M.-P. 2001. Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In Proc. Of the International Conference on Computer Vision, vol. 1, 105–112.

    [8] Rother, C., Kolmogorov, V., and Blake, A. 2004. Grabcut – interactive object extraction using iterated graph cuts. Proc. ACM Siggraph.

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    [10] Vezhnevets V and Konushin V, "GrowCut" - Interactive Multi-Label N-D Image Segmentation By Cellular Automata, in Proc. Graphicon, 2005, 150–156.

    [11] S. Yu and M. Berthod, A game strategy approach for image labeling, Computer Vision and Image Understanding, vol. 61, No. 1, January, 1995, 32-37.

    [12] Guo dong Guo, Shan Yu and Song de Ma, An Image Labeling Algorithm Based on Cooperative Game Theory, Signal Processing Proceedings, vol.2, 1998,978 – 981.

    [13] R.C. Gonzalez and R.E. Woods, Digital Image Processing Third Edition, Pearson Prentice Hall, 2008.

    [14] Martin J. Osborne, an introduce to Game Theory, Oxford University Press, Inc., New York, 2004.

    [15] Dhruv Batra, Adarsh Kowdle, Devi Parikh, Jeibo Luo and Tsuhan Chen, “iCoseg: Interactive Co-segmentation with Intelligent Scribble Guidance”, Computer Vision and Pattern Recognition, 2010.

    [16] Lytro, Inc., Lytro Dataset, Retrieved May 1, 2012, from http://www.lytro.com/living-pictures/

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