Yuri Boykov Daniel Huttenlocher Computer Science Department Cornell...  Home/Search
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Abstract: We describe a new approach to feature-based object recognition, using maximum a posteriori (MAP) estimation under a Markov random field (MRF) model. The main advantage of this approach is that it allows explicit modeling of dependencies between individual features of an object model. For instance, it can capture the fact that unmatched features due to partial occlusion are generally spatially coherent rather than independent. Efficient computation of the MAP estimate in our framework can be... (Update)
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BibTeX entry: (Update)
Yuri Boykov and Daniel P. Huttenlocher. A new bayesian framework for object recognition. Technical Report, ncstrl.cornell/TR98-1713, 1998. http://citeseer.ist.psu.edu/boykov98new.html More
@techreport{ boykov98new,
author = "Yuri Boykov and Daniel Huttenlocher",
title = "A New Bayesian Framework for Object Recognition",
number = "TR98-1713",
month = "28,",
pages = "0",
year = "1998",
url = "citeseer.ist.psu.edu/boykov98new.html" }
Citations (may not include all citations):110 Markov Random Field Modeling in Computer Vision (context) - Li - 1995
63 Exact maximum a posteriori estimation for binary images (context) - Greig, Porteous et al. - 1989
36 Markov random fields with efficient approximations - Boykov, Veksler et al. - 1998
17 Efficient Visual Recognition Using the Hausdorff Distance (context) - Rucklidge - 1996
8 A new bayesian framework for object recognition - Boykov, Huttenlocher - 1998
7 A probabilistic formulation for Hausdorff matching - Olson - 1998
6 Monte carlo comparison of distance transform based matching .. (context) - Huttenlocher - 1997
3 Learning probabilistic appearance models for object recognit.. (context) - Pope, Lowe - 1996
2 Comparing images using the Hausdorf distance (context) - Huttenlocher, Klanderman et al. - 1993
1 Practical reliable bayesian recognition of 2D and 3D objects.. - Subrahmonia, Cooper et al. - 1996
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