This book summarizes the results of the editors' modeling-from-reality (MFR) project, which took place over the last decade. The goal of this probject was to develop techniques for modeling real objects and/or env...
ISBN:
(纸本)9780792375159;9781461507970
This book summarizes the results of the editors' modeling-from-reality (MFR) project, which took place over the last decade. The goal of this probject was to develop techniques for modeling real objects and/or environments into geometric and photometric models through computer vision techniques. By developing such techniques, time-consuming modeling processes currently under-taken by human programmers can be (semi-) automatically performed, and, as a result, one can drastically shorten the developing time of such virtual reality systems, reduce their developing cost, and widen their application areas.
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...
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ISBN:
(数字)9780306469961
ISBN:
(纸本)9780792373728;9780306469961
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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