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Computer vision = statistical models...

Zhu, Song-Chun.

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Computer vision = statistical models for marr's paradigm /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computer vision/ by Song-Chun Zhu, Ying Nian Wu.
其他題名:	statistical models for marr's paradigm /
作者:	Zhu, Song-Chun.
其他作者:	Wu, Ying Nian.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xiv, 357 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Computer vision - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-030-96530-3
ISBN:	9783030965303

Computer vision = statistical models for marr's paradigm /
Zhu, Song-Chun.

Computer visionstatistical models for marr's paradigm /[electronic resource] :by Song-Chun Zhu, Ying Nian Wu. - Cham :Springer International Publishing :2023. - xiv, 357 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9783030965303

Standard No.: 10.1007/978-3-030-96530-3doiSubjects--Topical Terms:

3630571
Computer vision
--Statistical methods.

LC Class. No.: TA1634

Dewey Class. No.: 006.370727

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505 0 $a 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.
520 $a 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.
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