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Preface.- About the Authors.- 1 Introduction.- 2 Statistics of Natural Images.- 3 Textures.- 4 Textons.- 5 Gestalt Laws and Perceptual Organizations.- 6 Primal Sketch: Integrating Textures and Textons.- 7 2.1D Sketch and Layered Representation.- 8 2.5D Sketch and Depth Maps.- 9 Learning about information Projection.- 10 Informing Scaling and Regimes of Models.- 11 Deep Images and Models.- 12 A Tale of Three Families: Discriminative, Generative and Descriptive Models.- Bibliography
Song-Chun Zhu is Chair Professor at Peking and Tsinghua Universities, Director of Beijing Institute for General Artificial Intelligence, and Founding Dean of School of Artificial Intelligence at Peking University. He received his M.S. degree and Ph.D. degree in computer science from Harvard University in 1994 and 1996 respectively, under the supervision of David Mumford. He joined UCLA in 2002 as an Associate Professor. He became a full professor at UCLA in 2006 and returned to China in 2020. While at UCLA, Zhu was the director of Vision, Cognition, Learning, and Autonomy (VCLA) Lab. His research areas include computer vision, statistical modeling, cognitive reasoning, robot autonomy and AI. He has received many awards for his research contributions, including Marr Prize in 2003, and Helmholtz Test-of-Time Award in 2013. He is a fellow of IEEE Computer Society.
Ying Nian Wu is a professor in Department of Statistics,UCLA. He received his A.M. degree and Ph.D. degree in statistics from Harvard University in 1994 and 1996 respectively, under the supervision of Donald Rubin. He was an assistant professor in Department of Statistics, University of Michigan from 1997 to 1999. He joined UCLA in 1999. He was an assistant professor from 1999 to 2001. He was an associate professor from 2001 to 2006. He has been a full professor since 2006. Wu’s research areas include representation learning, generative modeling, computer vision, computational neuroscience, and bioinformatics.


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