Please note: In order to keep Hive up to date and provide users with the best features, we are no longer able to fully support Internet Explorer. The site is still available to you, however some sections of the site may appear broken. We would encourage you to move to a more modern browser like Firefox, Edge or Chrome in order to experience the site fully.

Bayesian Approach to Image Interpretation, PDF eBook

Bayesian Approach to Image Interpretation PDF

Part of the The Springer International Series in Engineering and Computer Science series

PDF

Please note: eBooks can only be purchased with a UK issued credit card and all our eBooks (ePub and PDF) are DRM protected.

Description

Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas.
For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial.
For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable.
New ideas introduced in the book include:

  • New approach to image interpretation using synergism between the segmentation and the interpretation modules.
  • A new segmentation algorithm based on multiresolution analysis.
  • Novel use of the Bayesian networks (causal networks) for image interpretation.
  • Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework.
Useful in both the academic and industrial research worlds, BayesianApproach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition.

Information

Other Formats

Information

Also in the The Springer International Series in Engineering and Computer Science series  |  View all