Model-based image matching using location
Model-based image matching using location
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integer and combinatorial optimization
Integer and combinatorial optimization
Partial Shape Recognition: A Landmark-Based Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian approach to model matching with geometric hashing
Computer Vision and Image Understanding
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Statistical Approaches to Feature-Based Object Recognition
International Journal of Computer Vision
Aided and Automatic Target Recognition Based Upon Sensory Inputs From Image Forming Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predicting Performance of Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Formulation for Hausdorff Matching
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Statistical Model for Occluded Object Recognition
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Statistical Model for Human Face Detection Using Multi-Resolution Features
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Statistical approaches for partially occluded object recognition
Statistical approaches for partially occluded object recognition
A spatiotemporal neural network for recognizing partially occludedobjects
IEEE Transactions on Signal Processing
Probe-based automatic target recognition in infrared imagery
IEEE Transactions on Image Processing
Object matching algorithms using robust Hausdorff distance measures
IEEE Transactions on Image Processing
Dynamic Trees for Unsupervised Segmentation and Matching of Image Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpretation of complex scenes using dynamic tree-structure Bayesian networks
Computer Vision and Image Understanding
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In this paper, we present a new Bayesian framework for partially occluded object recognition based on matching extracted local features on a one-to-one basis with object features. We introduce two different statistical models for occlusion: one model assumes that each feature in the model can be occluded independent of whether any other features are occluded, whereas the second model uses spatially correlated occlusion to represent the extent of occlusion. Using these models, the object recognition problem reduces to finding the object hypothesis with largest generalized likelihood. We develop fast algorithms for finding the optimal one-to-one correspondence between scene features and object features to compute the generalized likelihoods under both models. We conduct experiments illustrating the differences between the two occlusion models using different quantitative metrics. We also evaluate the recognition performance of our algorithms using examples extracted from object silhouettes and synthetic aperture radar imagery, and illustrate the performance advantages of our approach over alternative algorithms.