Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research

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9781118115145: Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research

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

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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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