MARC ayrıntıları
| 000 -LEADER |
| fixed length control field |
03233 am a2200325 i 4500 |
| 001 - CONTROL NUMBER |
| control field |
42783 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
| fixed length control field |
120323s2012 nyua b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
| LC control number |
2012008187 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
| International Standard Book Number |
9781107011793 |
| Qualifying information |
hardback |
| 035 ## - SYSTEM CONTROL NUMBER |
| System control number |
(OCoLC) |
| 040 ## - CATALOGING SOURCE |
| Original cataloging agency |
DLC |
| Transcribing agency |
DLC |
| Modifying agency |
DLC |
| -- |
BAUN |
| Description conventions |
rda |
| Language of cataloging |
eng |
| 049 ## - LOCAL HOLDINGS (OCLC) |
| Holding library |
BAUN_MERKEZ |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
| Classification number |
TA1634 |
| Item number |
.P75 2012 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
| Personal name |
Prince, Simon J. D. |
| Fuller form of name |
(Simon Jeremy Damion), |
| Dates associated with a name |
1972- |
| 245 10 - TITLE STATEMENT |
| Title |
Computer vision : |
| Remainder of title |
models, learning, and inference / |
| Statement of responsibility, etc |
Simon J.D. Prince. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Place of production, publication, distribution, manufacture |
New York : |
| Name of producer, publisher, distributor, manufacturer |
Cambridge University Press, |
| Date of production, publication, distribution, manufacture, or copyright notice |
[2012] |
|
| Date of production, publication, distribution, manufacture, or copyright notice |
©2012 |
| 300 ## - PHYSICAL DESCRIPTION |
| Extent |
xi, 580 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
26 cm. |
| 336 ## - CONTENT TYPE |
| Source |
rdacontent |
| Content Type Term |
text |
| Content Type Code |
txt |
| 337 ## - MEDIA TYPE |
| Source |
rdamedia |
| Media Type Term |
unmediated |
| Media Type Code |
unmediated |
| 338 ## - CARRIER TYPE |
| Source |
rdacarrier |
| Carrier Type Term |
volume |
| Carrier Type Code |
volume |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
| Bibliography, etc |
Includes bibliographical references (pages 533-566) and index. |
| 505 8# - FORMATTED CONTENTS NOTE |
| Title |
Machine generated contents note |
| -- |
Part I. Probability |
| -- |
1. Introduction to probability; 2. Common probability distributions; 3. Fitting probability models; 4. The normal distribution; Part II. Machine Learning for Machine Vision |
| -- |
5. Learning and inference in vision; 6. Modeling complex data densities; 7. Regression models; 8. Classification models; Part III. Connecting Local Models |
| -- |
9. Graphical models; 10. Models for chains and trees; 11. Models for grids; Part IV. Preprocessing |
| -- |
12. Image preprocessing and feature extraction; Part V. Models for Geometry |
| -- |
13. The pinhole camera; 14. Models for transformations; 15. Multiple cameras; Part VI. Models for Vision |
| -- |
16. Models for style and identity; 17. Temporal models; 18. Models for visual words; Part VII. Appendices |
| -- |
A. Optimization; B. Linear algebra; C. Algorithms. |
| 520 ## - SUMMARY, ETC. |
| Summary, etc |
"This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. [bullet] Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry [bullet] A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking [bullet] More than 70 algorithms are described in sufficient detail to implement [bullet] More than 350 full-color illustrations amplify the text [bullet] The treatment is self-contained, including all of the background mathematics [bullet] Additional resources at www.computervisionmodels.com"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name as entry element |
Computer vision. |
|
| Topical term or geographic name as entry element |
COMPUTERS / Computer Graphics. |
| Source of heading or term |
bisacsh |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
| 9 (RLIN) |
72911 |
| Corporate name or jurisdiction name as entry element |
Cambridge University Press. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Kitap |