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RT Book, Whole SR Electronic DC OPAC T1 Computer Vision : Statistical Models for Marr's Paradigm / by Song-Chun Zhu, Ying Nian Wu A1 Zhu, Song-Chun A1 Wu, Ying Nian A1 SpringerLink (Online service) YR 2023 FD 2023 SP XIV, 357 p. 192 illus., 109 illus. in color K1 Image processing -- Digital techniques K1 Computer vision K1 Information visualization K1 Computer science K1 Computer science -- Mathematics K1 Mathematical statistics K1 Neural networks (Computer science) K1 Computer Imaging, Vision, Pattern Recognition and Graphics K1 Data and Information Visualization K1 Theory of Computation K1 Probability and Statistics in Computer Science K1 Computer Science K1 Mathematical Models of Cognitive Processes and Neural Networks ED 1st ed. 2023. PB Springer International Publishing : Imprint: Springer PP Cham SN 9783030965303 LA English (英語) CL LCC:TA1501-1820 CL LCC:TA1634 CL DC23:006 NO 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 NO 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 NO HTTP:URL=https://doi.org/10.1007/978-3-030-96530-3 NO 書誌ID=EB00003951; LK [E Book]https://doi.org/10.1007/978-3-030-96530-3 OL 30