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商品詳細
Bayesian Approach to Image Interpretation. Softcover reprint of the original 1st ed. 2001
・ISBN 978-1-4757-7483-2 paper EUR 149.99
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お気に入り
★★★
| 著者・編者 | Kopparapu, Sunil K. / Desai, Uday B., |
|---|---|
| シリーズ | (The Springer International Series in Engineering and Computer Science) |
| 出版社 | (Springer-Verlag New York Inc., US) |
| 出版年月 | 2013 |
| ページ数 | 127 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-28002＞ |
解説
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:
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.