DocumentCode :
2611315
Title :
MAP model matching
Author :
Well, W.M.
Author_Institution :
Artificial Intelligence Lab., MIT, Cambridge, MA
fYear :
1991
fDate :
3-6 Jun 1991
Firstpage :
486
Lastpage :
492
Abstract :
A simple MAP model-matching criterion that captures important aspects of recognition in controlled situations is described. A detailed metrical object model is assumed. A probabilistic model of image features is combined with a simple prior on both the pose and the feature interpretations to yield a mixed objective function. The parameters that appear in the probabilistic models can be derived from images in the application domain. By extremizing the objective function, an optimal matching between model and image feature results. Within this framework, good models of feature uncertainty allow for robustness despite inaccuracy in feature detection. In addition, the relative likelihood of features arising from either the object or the background can be evaluated in a rational way. The objective function provides a simple and uniform means of evaluating match and pose hypotheses. Several linear projection and feature models are discussed. An experimental implementation of MAP model matching, among features derived from low-resolution edge images, is described
Keywords :
pattern recognition; picture processing; probability; MAP model-matching criterion; controlled situations; feature interpretations; image features; low-resolution edge images; metrical object model; mixed objective function; optimal matching; probabilistic model; recognition; Algorithm design and analysis; Artificial intelligence; Computer vision; Contracts; Image resolution; Image restoration; Laboratories; Research initiatives; Robustness; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1991. Proceedings CVPR '91., IEEE Computer Society Conference on
Conference_Location :
Maui, HI
ISSN :
1063-6919
Print_ISBN :
0-8186-2148-6
Type :
conf
DOI :
10.1109/CVPR.1991.139740
Filename :
139740
Link To Document :
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