An introduction to splines for use in computer graphics & geometric modeling
An introduction to splines for use in computer graphics & geometric modeling
Active vision
A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern theory: a unifying perspective
Perception as Bayesian inference
Learning to estimate scenes from images
Proceedings of the 1998 conference on Advances in neural information processing systems II
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Image Synthesis from a Single Example Image
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
X Vision: Combining Image Warping and Geometric Constraints for Fast Visual Tracking
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Learning Graphical Models of Images, Videos and Their Spatial Transformations
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Region tracking through image sequences
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A General Framework for Combining Visual Trackers --- The "Black Boxes" Approach
International Journal of Computer Vision
Building Models of Animals from Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
A Bayesian Framework for Extracting Human Gait Using Strong Prior Knowledge
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric and light-field deformable models
Computer Vision and Image Understanding
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating 3D hand pose using hierarchical multi-label classification
Image and Vision Computing
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Human Motion Tracking with a Kinematic Parameterization of Extremal Contours
International Journal of Computer Vision
Contour graph based human tracking and action sequence recognition
Pattern Recognition
Tracking and recognizing actions of multiple hockey players using the boosted particle filter
Image and Vision Computing
Efficient illumination independent appearance-based face tracking
Image and Vision Computing
A Single Camera Motion Capture System for Human-Computer Interaction
IEICE - Transactions on Information and Systems
Autonomous Robots
Efficient particle filtering using RANSAC with application to 3D face tracking
Image and Vision Computing
Computer Vision and Image Understanding
3D model based expression tracking in intrinsic expression space
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Finding human poses in videos using concurrent matching and segmentation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Shape prior embedded geodesic distance transform for image segmentation
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Segmentation-free, area-based articulated object tracking
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Particle Filtering with Region-based Matching for Tracking of Partially Occluded and Scaled Targets
SIAM Journal on Imaging Sciences
Exemplar-Based human contour tracking
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Template-Based hand pose recognition using multiple cues
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Multivariate relevance vector machines for tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Template-based hand detection and tracking
ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics
A new approach to human motion sequence recognition with application to diving actions
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors
International Journal of Computer Vision
Shape priors extraction and application for geodesic distance transforms in images and videos
Pattern Recognition Letters
Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization
The Visual Computer: International Journal of Computer Graphics
Robotics and Autonomous Systems
Computer Vision and Image Understanding
| Hi-index | 0.00 |
A new, exemplar-based, probabilistic paradigm for visual tracking is presented. Probabilistic mechanisms are attractive because they handle fusion of information, especially temporal fusion, in a principled manner. Exemplars are selected representatives of raw training data, used here to represent probabilistic mixture distributions of object configurations. Their use avoids tedious hand-construction of object models, and problems with changes of topology.Using exemplars in place of a parameterized model poses several challenges, addressed here with what we call the “Metric Mixture” (M2) approach, which has a number of attractions. Principally, it provides alternatives to standard learning algorithms by allowing the use of metrics that are not embedded in a vector space. Secondly, it uses a noise model that is learned from training data. Lastly, it eliminates any need for an assumption of probabilistic pixelwise independence.Experiments demonstrate the effectiveness of the M2 model in two domains: tracking walking people using “chamfer” distances on binary edge images, and tracking mouth movements by means of a shuffle distance.