Active shape models—their training and application
Computer Vision and Image Understanding
Pattern theory: a unifying perspective
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Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Face recognition by elastic bunch graph matching
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Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Object Localisation in Images
International Journal of Computer Vision
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Monte Carlo Statistical Methods (Springer Texts in Statistics)
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Representation and matching of articulated shapes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Representation and detection of deformable shapes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Wavelet-based SAR image despeckling and information extraction, using particle filter
IEEE Transactions on Image Processing
Bottom-up and top-down object matching using asynchronous agents and a contrario principles
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Improve matching results for structure from motion problems
International Journal of Computer Applications in Technology
Graph matching via sequential monte carlo
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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We consider the problem of locating instances of a known object in a novel scene by matching the fiducial features of the object. The appearance of the features and the shape of the object are modeled separately and combined in a Bayesian framework. In this paper, we present a novel matching scheme based on Sequential Monte Carlo, in which the features are matched sequentially, utilizing the information about the locations of previously matched features to constrain the task. The particle representation of hypotheses about the object position allow matching in multimodal and cluttered environments, where batch algorithms may have convergence difficulties. The proposed method requires no initialization or predetermined matching order, as the sequence can be started from any feature. We also utilize a Bayesian model to deal with features that are not detected due to occlusions or abnormal appearance. In our experiments, the proposed matching system shows promising results, with performance equal to batch approaches when the target distribution is unimodal, while surpassing traditional methods under multimodal conditions. Using the occlusion model, the object can be localized from only a few visible features, with the nonvisible parts predicted from the conditional prior model.