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140122 ||| eng |
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|a 9780306469961
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|a Kopparapu, Sunil K.
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|a Bayesian Approach to Image Interpretation
|h Elektronische Ressource
|c by Sunil K. Kopparapu, Uday B. Desai
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| 250 |
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|a 1st ed. 2000
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| 260 |
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|a New York, NY
|b Springer US
|c 2000, 2000
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| 300 |
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|a XV, 127 p
|b online resource
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| 505 |
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|a Overview -- Background -- MRF Framework For Image Interpretation -- Bayesian Net Approach to Image Interpretation -- Joint Segmentation and Image Interpretation -- Conclusions
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| 653 |
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|a Computer Communication Networks
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| 653 |
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|a Computer graphics
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| 653 |
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|a Image processing / Digital techniques
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| 653 |
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|a Computer vision
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| 653 |
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|a Computer Graphics
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| 653 |
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|a Computer Vision
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| 653 |
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|a Computer networks
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| 653 |
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|a Computer Imaging, Vision, Pattern Recognition and Graphics
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| 700 |
1 |
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|a Desai, Uday B.
|e [author]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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|a The Springer International Series in Engineering and Computer Science
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| 028 |
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|a 10.1007/b117231
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| 856 |
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|u https://doi.org/10.1007/b117231?nosfx=y
|x Verlag
|3 Volltext
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|a 006.37
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| 520 |
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|a 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, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition
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