Unsupervised Colorization of Black-and-White Cartoons
We present a novel color-by-example technique which combines image segmentation, patch-based sampling and probabilistic reasoning. This method is able to automate colorization when new color information is applied on the already designed black-and-white cartoon. Our technique is especially suitable for cartoons digitized from classical celluloid ?lms, which were originally produced by a paper or cel based method. In this case, the background is usually a static image and only the dynamic foreground needs to be colored frame-by-frame. We also assume that objects in the foreground layer consist of several well visible outlines which will emphasize the shape of homogeneous regions.