Computer Vision: Statistical Models for Marr's Paradigm

As the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The authors hope to provide foundation and, perhaps more importantly, further inspiration for continued research in vision. This book covers David Marr's paradigm and various underlying statistical models for vision. The mathematical framework herein integrates three regimes of models (low-, mid-, and high-entropy regimes) and provides foundation for research in visual coding, recognition, and cognition. Concepts are first explained for understanding and then supported by findings in psychology and neuroscience, after which they are established by statistical models and associated learning and inference algorithms. A reader will gain a unified, cross-disciplinary view of research in vision and will accrue knowledge spanning from psychology to neuroscience to statistics.

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.

宋春柱是北京大學和清華大學的講座教授，北京智能科技研究院的院長，以及北京大學人工智能學院的創院院長。他於1994年和1996年分別在哈佛大學獲得計算機科學碩士學位和博士學位，並在David Mumford的指導下完成學業。他於2002年加入加州大學洛杉磯分校（UCLA）擔任副教授，並於2006年晉升為正教授。他於2020年回到中國。在UCLA期間，宋教授擔任視覺、認知、學習和自主性（VCLA）實驗室的主任。他的研究領域包括計算機視覺、統計建模、認知推理、機器人自主性和人工智能。他因其研究貢獻獲得了許多獎項，包括2003年的Marr獎和2013年的Helmholtz時間考驗獎。他是IEEE計算機學會的會士。

吳英年是加州大學洛杉磯分校統計學系的教授。他於1994年和1996年分別在哈佛大學獲得統計學碩士學位和博士學位，並在Donald Rubin的指導下完成學業。他曾在密歇根大學統計學系擔任助理教授（1997年至1999年），並於1999年加入UCLA。他在UCLA擔任助理教授（1999年至2001年）和副教授（2001年至2006年），自2006年起擔任正教授。吳教授的研究領域包括表示學習、生成建模、計算機視覺、計算神經科學和生物信息學。