Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research
Author(s): Jun Yu (Author), Dacheng Tao (Author)
Publisher: Wiley-IEEE Press
Publication Date: 26 April 2013
Edition: 1st
Language: English
Print length: 208 pages
ISBN-10: 9781118115145
ISBN-13: 9781118115145
Book Description
The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations
Editorial Reviews
From the Inside Flap
Helps readers learn the latest machine learning techniques and presents their applications in cartoon animation research
Machine learning techniques have been widely used in many fields including machine perception, computer vision, natural language processing, syntactic pattern recognition, and search engines. Recently, many modern techniques have been proposed in machine learning and the integration of these techniques and cartoon animation research is fast becoming a hot topic.
This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations.
Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research covers:
Manifold, semisupervised, and multiview learning
Example-based motion reuse
Crowd and facial animation
Discriminative locality alignment
Spectral clustering and graph cut
SVM with multiple unweighted sum kernels
Hypothesis space selection
Cartoon texture and reuse systems for animation synthesis
Video clip reuse
Stroke correspondence construction via stroke
Cartoon character extraction
Skeleton feature
Cartoon clip synthesis
From the Back Cover
Helps readers learn the latest machine learning techniques and presents their applications in cartoon animation research
Machine learning techniques have been widely used in many fields including machine perception, computer vision, natural language processing, syntactic pattern recognition, and search engines. Recently, many modern techniques have been proposed in machine learning and the integration of these techniques and cartoon animation research is fast becoming a hot topic.
This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations.
Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research covers:
Manifold, semisupervised, and multiview learning
Example-based motion reuse
Crowd and facial animation
Discriminative locality alignment
Spectral clustering and graph cut
SVM with multiple unweighted sum kernels
Hypothesis space selection
Cartoon texture and reuse systems for animation synthesis
Video clip reuse
Stroke correspondence construction via stroke
Cartoon character extraction
Skeleton feature
Cartoon clip synthesis
About the Author
JUN YU, PhD, is an Associate Professor in the Computer Science Department, School of Information Science and Engineering, Xiamen University, Xiamen, China. His current research interests include computer graphics, computer visions, and machine learning. He has authored or coauthored more than thirty scientific articles in journals including IEEE T-IP, IEEE TSMC-B, and Pattern Recognition.
DACHENG TAO, PhD, is Professor of Computer Science with the Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering & Information Technology, University of Technology, Sydney, Australia. His research applies statistics and mathematics to data analysis problems in computer vision, data mining, machine learning, multimedia, and video surveillance. He has authored more than 100 scientific articles.
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