Computer vision : algorithms and applications / Richard Szeliski.
2022
TA1634 .S94 2022
Linked e-resources
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Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Computer vision : algorithms and applications / Richard Szeliski.
Edition
Second edition.
ISBN
9783030343729 (electronic bk.)
3030343723 (electronic bk.)
9783030343712
3030343715
3030343723 (electronic bk.)
9783030343712
3030343715
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (some color).
Item Number
10.1007/978-3-030-34372-9 doi
Call Number
TA1634 .S94 2022
Dewey Decimal Classification
006.3/7
Summary
Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. More than just a source of "recipes" this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles. Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision. About the Author Dr. Richard Szeliski has more than 40 years experience in computer vision research, most recently at Facebook and Microsoft Research, where he led the Computational Photography and Interactive Visual Media groups. He is currently an Affiliate Professor at the University of Washington where he co-developed (with Steve Seitz) the widely adopted computer vision curriculum on which this book is based.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 12, 2022).
Series
Texts in computer science.
Available in Other Form
Print version: 9783030343712
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
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All Resources
Table of Contents
1 Introduction
2 Image Formation
3 Image Processing
4 Model Fitting and Optimization
5 Deep Learning
6 Recognition
7 Feature Detection and Matching
8 Image Alignment and Stitching
9 Motion Estimation
10 Computational Photography
11 Structure from Motion and SLAM
12 Depth Estimation
13 3D Reconstruction
14 Image-Based Rendering
15 Conclusion
Appendix A: Linear Algebra and Numerical Techniques
Appendix B: Bayesian Modeling and Inference
Appendix C: Supplementary Material.
2 Image Formation
3 Image Processing
4 Model Fitting and Optimization
5 Deep Learning
6 Recognition
7 Feature Detection and Matching
8 Image Alignment and Stitching
9 Motion Estimation
10 Computational Photography
11 Structure from Motion and SLAM
12 Depth Estimation
13 3D Reconstruction
14 Image-Based Rendering
15 Conclusion
Appendix A: Linear Algebra and Numerical Techniques
Appendix B: Bayesian Modeling and Inference
Appendix C: Supplementary Material.