國立高雄大學圖資館 |

登入

回首頁

Computer visionstatistical models for marr's paradigm /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computer visionby 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.
Contained By:	Springer Nature eBook
標題:	Computer visionStatistical methods.
電子資源:	https://doi.org/10.1007/978-3-030-96530-3
ISBN:	9783030965303$q(electronic bk.)

Computer visionstatistical 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$q(electronic bk.)

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

949828
Computer vision
--Statistical methods.

LC Class. No.: TA1634

Dewey Class. No.: 006.370727

Computer visionstatistical models for marr's paradigm /
LDR:02452nmm a2200325 a 4500 001 642475
003 DE-He213
005 20230315143856.0
006 m d
007 cr nn 008maaau
008 231123s2023 sz s 0 eng d
020 $a 9783030965303$q(electronic bk.)
020 $a 9783030965297$q(paper)
024 7 $a 10.1007/978-3-030-96530-3 $2 doi
035 $a 978-3-030-96530-3
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1634
072 7 $a UYT $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYT $2 thema
082 0 4 $a 006.370727 $2 23
090 $a TA1634 $b .Z63 2023
100 1 $a Zhu, Song-Chun. $3 862942
245 1 0 $a Computer vision $h [electronic resource] : $b statistical models for marr's paradigm / $c by Song-Chun Zhu, Ying Nian Wu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xiv, 357 p. : $b ill. (some col.), digital ; $c 24 cm.
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.
650 0 $a Computer vision $x Statistical methods. $3 949828
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
650 2 4 $a Data and Information Visualization. $3 913414
650 2 4 $a Theory of Computation. $3 274475
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
650 2 4 $a Computer Science. $3 212513
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
700 1 $a Wu, Ying Nian. $3 949827
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-96530-3
950 $a Computer Science (SpringerNature-11645)