Interactive cartoon reusing by transfer learning
Signal Processing
Image classification by multimodal subspace learning
Pattern Recognition Letters
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In this paper, we propose a novel approach, which reuses Traditional Chinese Cartoon to create new animations. In order to extract the cartoon character precisely, a segmentation method based on edge detection is implemented. Before reusing the data, a lower- dimensional space of the cartoon data is constructed by ISOmap. The character's gesture difference calculated by optical flow is combined with character's edge difference through a novel distance function, which is controlled by a weight parameter. The animation is created by reordering the existing data into a sequence, which is the shortest path between two designated data in the space. Our approach utilizes image processing, computer vision, and machine learning in cartoon creation and the experiment results demonstrate that the animation's quality can be effectively improved by the fusion of these techniques. Copyright © 2007 John Wiley & Sons, Ltd.