Paper Infomation
Shadow Editing Method based on Supervised Learning
Full Text(PDF, 541KB)
Author: Chunjing Si, Chongzhi Xu
Abstract: In the process of acquisition and utilization of intangible cultural heritage, videos captured from different angles on intangible cultural heritage are always needed. But due to the insufficient illumination, there are all kinds of soft shadows on the videos. In order to remove and edit these soft shadows, we put forward to a method of shadow editing based on supervised learning. The method neither assumes the existence of umbra special model nor a unified model to deal with the whole piece of shadow, but provides the user with the complete control who are allowed to choose the area which needs modifying. We also can handle some complex problems that those previous technologies can't do, for example the shadows are all penumbra, as is often the case (such as the projection of the shadow of the leaves on the ground). Our technology requires user interaction of determining which area of the image needs to be modified. Then the system is initialized at once, then our model is applied. Once the shadow side is calculated, the user can interactively operate them, or use our simple interface to modify the rest of the images.
Keywords: Soft Shadow Image Edit, Supervised Learning
References:
[1] ARBEL, E. AND HEL-OR, H. 2011. Shadow removal using intensity surfaces and texture anchor points. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 6.
[2] BARNES, C., SHECHTMAN, E., FINKELSTEIN, A., AND GOLDMAN, D. B. 2009. PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Transactions on Graphics (Proc. SIGGRAPH) 28, 3.
[3] BARRON, J. T. AND MALIK, J. 2012. Color constancy, intrinsic images, and shape estimation. In ECCV.
[4] BARROW, H. AND TENENBAUM, J. 1978. Recovering intrinsic scene characteristics from images. Computer Vision Systems.
[5] BOUSSEAU, A., PARIS, S., AND DURAND, F. 2009. User assisted intrinsic images. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 28, 5.
[6] BOYADZHIEV, I., PARIS, S., AND KAVITA, B. 2013. User-assisted image compositing for photographic lighting. ACM Transactions on Graphics (Proc. SIGGRAPH).
[7] BOYKOV, Y., VEKSLER, O., AND ZABIH, R. 2001. Efficient approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 12.
[8] BREIMAN, L., FRIEDMAN, J., STONE, C. J., AND OLSHEN, R. A. 1984. Classification and regression trees. Chapman & Hall/CRC.
[9] GUO, R., DAI, Q., AND HOIEM, D. 2012. Paired regions for shadow detection and removal. IEEE Transactions on Pattern Analysis and Machine Intelligence.
[10] MOHAN, A., TUMBLIN, J., AND CHOUDHURY, P. 2007. Editing soft shadows in a digital photograph. IEEE Computer Graphics and Applications 27, 2.
[11] REYNOLDS, M., DOBOˇS , J., PEEL, L., WEYRICH, T., AND BROSTOW, G. J. 2011. Capturing time-of-flight data with confidence. In CVPR.
[12] TANG, K., YANG, J., AND WANG, J. 2014. Investigating haze-relevant features in a learning framework for image dehazing. In CVPR.
[13] KRUSCHKE, J. K. 2011. Doing Bayesian Data Analysis. Academic Press.