Computer Vision : Statistical Models for Marr's Paradigm

Ying Nian Wu

ISBN 10: 3030965325 / ISBN 13: 9783030965327

Published by Springer International Publishing, Springer Nature Switzerland, 2024

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030965327

Bibliographic Details

• Title: Computer Vision : Statistical Models for Marr's Paradigm
• Publisher: Springer International Publishing, Springer Nature Switzerland
• Publication Date: 2024
• Language: English
• ISBN 10: 3030965325
• ISBN 13: 9783030965327
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 21D millimeters 235 millimeters width by 155 millimeters height by 21 millimeters depth
• Weight: 569 grams

About this title

Synopsis

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. 

About the Author

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.