Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research

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

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