TY  - BOOK
AU  - Zhu,Song-Chun
AU  - Wu,Ying Nian
ED  - SpringerLink (Online service)
TI  - Computer Vision: Statistical Models for Marr's Paradigm 
SN  - 9783030965303
AV  - TA1501-1820 
U1  - 006 23
PY  - 2023///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Image processing
KW  - Digital techniques
KW  - Computer vision
KW  - Information visualization
KW  - Computer science
KW  - Mathematics
KW  - Mathematical statistics
KW  - Neural networks (Computer science) 
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
KW  - Data and Information Visualization
KW  - Theory of Computation
KW  - Probability and Statistics in Computer Science
KW  - Computer Science
KW  - Mathematical Models of Cognitive Processes and Neural Networks
N1  - Acceso multiusuario; 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-030-96530-3
ER  -