當人們觀賞一張照片時，會常常想像在照片中的影像如果動起來會是什麼樣，想像力讓我們大腦接收到的資訊超乎一張靜態影像能給予我們的，在電腦視覺這門科學中，如何在靜態影像中加入動態的畫面的研究也正持續進行著。我們提出了一個能夠合成水流動畫到單張影像的系統，讓編輯者選擇想要編輯的影像區域和來源影片。我們分析水流的方法整合了線卷積與光流法，將影像與影片轉換成具有水流流向特徵的紋理貼圖，藉由分析此紋理貼圖來比對水流的相似程度，可以減少影像中許多容易影響比對的因素，像是顏色、明暗等等的。在做合成之前將來源影片的顏色外觀轉換成與目標影像相似，可以減少找不到適合的接縫的機率，本篇論文最後實作的紋理合成的演算法是基於影像縫合與圖切割的概念來完成，最後在套上多重解析度影像融合在人為痕跡明顯的拼接處，將不連續感進一步消除。

When people view a picture, they often imagine how the dynamic object in picture would move. The imagination makes people perceive much more than a static object before them and adding the motion in static image is always an active area in computer graphics. This paper present a system for synthesizing fluid motion on a single image. We include multiple relative techniques of image processing and computer vision.
The flow animation is extracted from a related video sequence and pasted onto the target image. For reducing the complexity of our system, we use a semi-automatic approach to let the user control the target image and the source video to create desired result. We build a simple interface for the following user-defined process. The user manually specifies the interested region of target image along with segmentation by grab-cut and choose the reference video. The user semi-automatically set the trimap for alpha matting to extract the foreground of video. Then, the system automatically executes remain process.
Our method integrates the optical flow and the line integral convolution to transfer the target image and video to textures that has correlation of pixels along the flow. The flow field of video can be obtained by compare the consecutive frame but the for single image there has no pervious frame can be analysis. We simply calculate the gradient of segmented target to generate the approximate flow field. The fluid orientation between image and video can be compared without the color’s influence by using the texture-based flow image.
The synthesis algorithm we purposed in this paper is based on the idea of image quilting and the graph-cut. The matching process depends on the flow texture but the synthesis process uses the original texture. The weight computation of seam finding on each node is refer to the color difference. Before synthesizing, we transfer color appearance of the video frame to be like the target image that can decrease the probability for visible seams in the generated result. Finally, we refine the appearance of the result by blending or blurring the seam.

[1] Peter Litwinowicz and Lance Williams. Animating images with drawings. In Proceedings of SIGGRAPH ’94, pp. 409–412, 1994.
[2] William A. Barrett and Alan S. Cheney. Object-based image editing. In Proceedings of SIGGRAPH, pp. 777–784. 2002.
[3] Yung-Yu Chuang, Dan B. Goldman, Ke Colin Zheng, Brian Curless, David H. Salesin, and Richard Szeliski. Animating pictures with stochastic motion textures. In Proceedings of SIGGRAPH, pp. 853–860, 2005.
[4] Tomer Moscovich and John F. Hughes. As-rigid-as-possible shape manipulation. In Proceedings of SIGGRAPH, pp. 1134–1141, 2005.
[5] Youichi Horry, Ken-ichi Anjyo, and Kiyoshi Arai. Tour into the picture: Using a spidery mesh interface to make animation from a single image. In Proceedings of SIGGRAPH ’97, pp. 225-232. 1997.
[6] Hornung, A., Dekkers E., and Kobbelt, L. Character animation from 2d pictures and 3d motion data. ACM Trans. Graph. 26, 1, 2007.
[7] J. F. David and W. Yair, Optical Flow Estimation in Paragios et al. Handbook of Mathematical Models in Computer Vision, Springer, ISBN 0-387-26371-3, 2006.
[8] S. S. Beauchemin and J. L. Barron. The computation of optical flow. ACM New York, USA, 1995.
[9] Carsten Rother, Vladimir Kolmogorov, and Andrew Blake. Grabcut – Interactive foreground extraction using iterated graph cuts. In Proceedings of SIGGRAPH, 2004.
[10] Kaiming He, Christoph Rhemann, Carsten Rother, Xiaoou Tang, and Jian Sun. A global sampling method for alpha matting. CVPR, 2011.
[11] G. Farneback. Two-frame motion estimation based on polynomial expansion. Proceedings of the 13th SCIA, pp. 363–370, 2003.
[12] Brian Cabral and Leith (Casey) Leedom. Imaging vector fields using line integral convolution. SIGGRAPH’93, Proceedings of the 20th annual conference on Computer graphics and interactive techniques, pp. 263-270, 1993.
[13] Alexei A. Efros and William T. Freeman. Image quilting for texture synthesis and transfer. Proceedings of SIGGRAPH, pp. 341–346. ISBN 1-58113-292-1, 2001.
[14] Vivek Kwatra, Arno Schödl, Irfan Essa, Greg Turk, and Aaron Bobick. Graphcut textures: Image and video synthesis using graph cuts. ACM Transactions on Graphics, SIGGRAPH, 22, 3, pp. 277-286, 2003.
[15] Jeremy S. De Bonet. Multiresolution sampling procedure for analysis and synthesis of texture images. Proceedings of SIGGRAPH 97, pp. 361–368. ISBN 0-89791-896-7. Held in Los Angeles, California, 1997.
[16] Alexei A. Efros and Thomas K. Leung. Texture synthesis by non-parametric sampling. In International Conference on Computer Vision. Vol.2, pp. 1033-1038, 1999.
[17] Li-Yi Wei and Marc Levoy. Fast texture synthesis using tree structured vector quantization. Proceedings of SIGGRAPH, pp. 479-488, 2000.
[18] L. Ford and D. Fulkerson. Flow in networks. Princeton University Press, 1962.
[19] Erik Reinhard, Michael Ashikhmin, Bruce Gooch, and Peter Shirley. Color transfer between images. IEEE Comput. Graph.,Appl. 21, 5, pp. 34–41, 2001.
[20] R. Brunelli. Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, ISBN 978-0-470-51706-2, 2009.
[21] Steven L. Kilthau, Mark S. Drew, and Torsten Möller. Full search content independent matching based on the fast fourier transform. 2002 Conference on Image Processing, vol. 1, pp. 669-672, 2002.
[22] J.L. Barron, D.J. Fleet, and S.S. Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, pp. 43-77, 1994.
[23] Horn B.K.P. and Schunck B.G. Determining optical flow. Artificial Intelligence, vol 17, pp. 185-203, 1981.
[24] B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. in Proceedings of Imaging Understanding Workshop, pp. 121–130, 1981.
[25] T. Porter and T. Duff. Compositing digital images. In Proceeding of ACM SIGGRAPH, pp. 253-259, July 1984.
[26] Jue Wang and Michael F. Cohen. Image and video matting. Now Pubishers Inc., Hanover, MA, USA, 2008.
[27] Rudolf Netzel and Daniel Weiskopf. Texture-based flow visualization. Computing in Science & Engineering, vol. 15, no., pp.96-102, Nov.-Dec. 2013.
[28] Peter J. Burt and Edward H. Adelson. A multiresolution spline with application to image mosaics. ACM Transaction on Graphics, 2, 4, pp. 217-236, 1983.
[29] R.C. Gonzalex and R.E. Woods, Digital Image Processing (3ed ed.), Prentice-Hall, Inc.,Upper Saddle River, NJ, USA, 2006.