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Chapter 1 Introduction
Chapter 2 Image formation
Chapter 3 Image processing
Chapter 4 Model fitting and optimization
Chapter 5 Deep Learning
Chapter 6 Recognition
Chapter 7 Feature detection and matching
Chapter 8 Image alignment and stitching
Chapter 9 Motion estimation
Chapter 10 Computational photography
Chapter 11 Structure from motion and SLAM
Chapter 12 Depth estimation
Chapter 13 3D reconstruction
Chapter 14 Image-based rendering
Chapter 15 Conclusion
Appendix A Linear algebra and numerical techniques
Appendix B Bayesian modeling and inference
Appendix C Supplementary material
Index

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Computer vision : algorithms and applications 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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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.



Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?

Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.

More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques

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
  • Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
  • Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory
  • Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book
  • Supplies supplementary course material for students at the associated website, http://szeliski.org/Book/

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.



New feature

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.



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