The Springer International Series in Engineering and Computer Science

Bayesian Modeling of Uncertainty in Low-Level Vision

Authors: Szeliski, Richard

Free Preview

Buy this book

eBook 118,99 €
price for Spain (gross)
  • ISBN 978-1-4613-1637-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 155,95 €
price for Spain (gross)
  • ISBN 978-0-7923-9039-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 124,79 €
price for Spain (gross)
  • ISBN 978-1-4612-8904-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low­ level vision. Recently, probabilistic models have been proposed and used in vision. Sze­ liski's method has a few distinguishing features that make this monograph im­ portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of the method to several real world problems, including the generation of fractal surfaces, motion estimation without correspondence using sparse range data, and incremental depth from motion.

Table of contents (8 chapters)

Table of contents (8 chapters)

Buy this book

eBook 118,99 €
price for Spain (gross)
  • ISBN 978-1-4613-1637-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 155,95 €
price for Spain (gross)
  • ISBN 978-0-7923-9039-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 124,79 €
price for Spain (gross)
  • ISBN 978-1-4612-8904-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Bayesian Modeling of Uncertainty in Low-Level Vision
Authors
Series Title
The Springer International Series in Engineering and Computer Science
Series Volume
79
Copyright
1989
Publisher
Springer US
Copyright Holder
Kluwer Academic Publishers
eBook ISBN
978-1-4613-1637-4
DOI
10.1007/978-1-4613-1637-4
Hardcover ISBN
978-0-7923-9039-8
Softcover ISBN
978-1-4612-8904-3
Series ISSN
0893-3405
Edition Number
1
Number of Pages
XX, 198
Topics