Conference Paper

Video2Cartoon: generating 3D cartoon from broadcast soccer video.

DOI: 10.1145/1101149.1101184 Conference: Proceedings of the 13th ACM International Conference on Multimedia, Singapore, November 6-11, 2005
Source: DBLP


In this demonstration, a prototype system for generating 3D cartoon from broadcast soccer video is proposed. This system takes advantage of computer vision (CV) and computer graphics (CG) techniques to provide users new experience that can not be obtained from original video. Firstly, it uses CV techniques to obtain 3D positions of the players and ball. Then, CG techniques are applied to model the playfield, players, and ball. Finally, 3D cartoon is generated. Our system allows users to watch the game at any point of view using a 3D viewer based on OpenGL.

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Available from: Wen Gao, Jan 19, 2015
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    • "Animation is one of the commonly used media [1] [2] [3] [4] , which can communicate information via images. Traditional cartoon animation is a labour-intensive job, and many steps are repetitive and require few special skills. "
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    ABSTRACT: In this paper, a novel method called fuzzy diffusion maps (FDM) is proposed to evaluate cartoon similarity, which is critical to the applications of cartoon recognition, cartoon clustering and cartoon reusing. We find that the features from heterogeneous sources have different influence on cartoon similarity estimation. In order to take all the features into consideration, a fuzzy consistent relation is presented to convert the preference order of the features into preference degree, from which the weights are calculated. Based on the features and weights, the sum of the squared differences (L2) can be calculated between any cartoon data. However, it has been demonstrated in some research work that the cartoon dataset lies in a low-dimensional manifold, in which the L2 distance cannot evaluate the similarity directly. Unlike the global geodesic distance preserved in Isomap, the local neighboring relationship preserved in Locally Linear Embedding, and the local similarities of neighboring points preserved in Laplacian Eigenmaps, the diffusion maps we adopt preserve diffusion distance summing over all paths of length connecting the two data. As a consequence, this diffusion distance is very robust to noise perturbation. Our experiment in cartoon classification using Receiver Operating Curves shows fuzzy consistent relation's excellent performance on weights assignment. The FDM’s performance on cartoon similarity evaluation is tested on the experiments of cartoon recognition and clustering. The results show that FDM can evaluate the cartoon similarity more precisely and stably compared with other methods.
    Journal of Computer Science and Technology 03/2011; 26(2):203-216. DOI:10.1007/s11390-011-9427-4 · 0.67 Impact Factor
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    • "Second, as a bonus resulting from the increased accuracy of the projection matrix, we are able to decompose the projection matrix into camera intrinsic and extrinsic parameters , so that new applications are possible. This result outperforms estimating parameters from the homography mappings [4], leading to large errors. Because of the accuracy of the algorithm, our system allows professional applications besides the enhanced viewing experience (free viewpoint). "
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    ABSTRACT: This paper presents a new augmented-reality system designed to generate visual enhancements for TV broadcasted court-net sports. A probabilistic method based on the Expectation Maximization (EM) procedure is utilized to find the optimal feature points, thereby enabling the automatic acquisition of the camera parameters from the TV image with high accuracy. A virtual camera derived from the original camera, helps to synthesize a variety of virtual scenes, such as the scene from the viewpoint of a player, depending on the intention of the user. To preserve the visual nature of the original human motion, the player's shape and texture are extracted from the real video and texture-mapped onto the virtual video. The system was tested over a set of court-net sports videos containing tennis, badminton and volleyball and demonstrated promising results.
    Proceedings of the 15th International Conference on Multimedia 2007, Augsburg, Germany, September 24-29, 2007; 01/2007
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    ABSTRACT: With advances in broadcasting technologies, people are now able to watch videos on devices such as televisions, computers, and mobile phones. Scalable video provides video bitstreams of different size under different transmission bandwidths. In this paper, a semantic scalability scheme with four levels is proposed, and tennis videos are used as examples in experiments to test the scheme. Rather than detecting shot categories to determine suitable scaling options for Scalable Video Coding (SVC) as in previous studies, the proposed method analyzes a video, transmits video content according to semantic priority, and reintegrates the extracted contents in the receiver. The purpose of the lower bitstream size in the proposed method is to discard video content of low semantic importance instead of decreasing the video quality to reduce the video bitstream. The experimental results show that visual quality is still maintained in our method despite reducing the bitstream size. Further, in a user study, we show that evaluators identify the visual quality as more acceptable and the video information as clearer than those of SVC. Finally, we suggest that the proposed scalability scheme in the semantic domain, which provides a new dimension for scaling videos, can be extended to various video categories. KeywordsContent adaptive–Scalable video–Video rendering–Video analysis–Scalable video coding
    Multimedia Tools and Applications 07/2012; 59(2):1-15. DOI:10.1007/s11042-010-0685-x · 1.35 Impact Factor
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