Bayesian hypothesis generation and verification

被引：2
作者：
Armbruster W. [1 ]
机构：
[1] FGAN-FOM Research Institute for Optronics and Pattern Recognition, 76275 Ettlingen
来源：
Pattern Recognition and Image Analysis | 2008年 / 18卷 / 02期
关键词：
Bayesian formulation - Bayesian hypothesis - Hypothesis generation and verification - Likelihood functions - Model-based OPC - Optimal decision making - Posterior distributions - Segmentation results;
D O I：
10.1134/S1054661808020120
中图分类号：
学科分类号：
摘要：
Regarding computer vision as optimal decision making under uncertainty, a new optimization paradigm is introduced, namely, maximizing the product of the likelihood function and the posterior distribution on scene hypotheses given the results of feature extraction. Essentially this approach is a Bayesian formulation of hypothesis generation and verification. The approach is illustrated for model-based object recognition in range imagery, showing how segmentation results can optimally be incorporated into model matching. Several new match criteria for model based object recognition in range imagery are deduced from the theory. © 2008 Pleiades Publishing, Ltd.
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收藏
页码：269 / 274
页数：5
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