Bayesian Object Localisation in Images
International Journal of Computer Vision
Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Detection and Tracking of Human Motion with a View-Based Representation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Statistical Foreground Modelling for Object Localisation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Probabilistic Background Model for Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Sampling Algorithm for Tracking Multiple Objects
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Video Object Hyper-Links for Streaming Applications
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
The Nonlinear Statistics of High-Contrast Patches in Natural Images
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Learning the Statistics of People in Images and Video
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Learning-based tracking of complex non-rigid motion
Journal of Computer Science and Technology
Multi-dimensional visual tracking using scatter search particle filter
Pattern Recognition Letters
3D shape-encoded particle filter for object tracking and its application to human body tracking
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Computer Vision and Image Understanding
A sensor fusion framework using multiple particle filters for video-based navigation
IEEE Transactions on Intelligent Transportation Systems
Adaptive particle filter based on energy field for robust object tracking in complex scenes
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Switching observation models for contour tracking in clutter
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Tracking of multiple objects using optical flow based multiscale elastic matching
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Multi-object segmentation using shape particles
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
A local basis representation for estimating human pose from cluttered images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Hand detection by direct convexity estimation
ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics
A novel evidence based model for detecting dangerous situations in level crossing environments
Expert Systems with Applications: An International Journal
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Maximization of cross-correlation is a commonly used principle for intensity-based object localization that gives a single estimate of location. However, to facilitate sequential inference (eg over time or scale) and to allow the representation of ambiguity, it is desirable to represent an entire probability distribution for object location. Although the cross-correlation itself (or some function of it) has sometimes been treated as a probability distribution, this is not generally justifiable.Bayesian correlation achieves a consistent probabilistic treatment by combining several developments. The first is the interpretation of correlation matching functions in probabilistic terms, as observation likelihoods. Second, probability distributions of filter-bank responses are learned from training examples. Inescapably, response-learning also demands statistical modeling of background intensities, and there are links here with image coding and Independent Component Analysis. Lastly, multi-scale processing is achieved, in a Bayesian context, by means of a new algorithm, layered sampling, for which asymptotic properties are derived.