Tracking and data association
Image-flow computation: an estimation-theoretic framework and a unified perspective
CVGIP: Image Understanding
Performance of optical flow techniques
International Journal of Computer Vision
Robust computation of optical flow in a multi-scale differential framework
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Computation and analysis of image motion: a synopsis of current problems and methods
International Journal of Computer Vision
A General Motion Model and Spatio-Temporal Filters forComputing Optical Flow
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable and Efficient Computation of Optical Flow
International Journal of Computer Vision
International Journal of Computer Vision
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Sequence Evaluation: 30 Years and Still Going Strong
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
An Information Fusion Framework for Robust Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Ordinal regression based subpixel shift estimation for video super-resolution
EURASIP Journal on Advances in Signal Processing
Accurate Optical Flow Sensor for Obstacle Avoidance
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Probabilistic fusion-based parameter estimation for visual tracking
Computer Vision and Image Understanding
Optical-flow based on an edge-avoidance procedure
Computer Vision and Image Understanding
Low-Cost Gesture-Based Interaction for Intelligent Environments
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Learning sparse kernels from 3D surfaces for heart wall motion abnormality detection
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Automated heart wall motion abnormality detection from ultrasound images using Bayesian networks
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Information fusion for multi-camera and multi-body structure and motion
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Improving security in WMNs with reputation systems and self-organizing maps
Journal of Network and Computer Applications
Multibandwidth kernel-based object tracking
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
Journal of Network and Computer Applications
An adaptive method of tracking anatomical curves in x-ray sequences
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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The problem of information fusion appears in many forms in vision. Tasks such as motion estimation, multimodal registration, tracking, and robot localization, often require the synergy of estimates coming from multiple sources. Most of the fusion algorithms, however, assume a single source model and are not robust to outliers. If the data to be fused follow different underlying models, the traditional algorithms would produce poor estimates. We present in this paper a nonparametric approach to information fusion called Variable-Bandwidth Densitybased Fusion (VBDF). The fusion estimator is computed as the location of the most significant mode of a density function which takes into account the uncertainty of the estimates to be fused. A novel mode detection scheme is presented, which relies on variable-bandwidth mean shift computed at multiple scales. We show that the proposed estimator is consistent and conservative, while handling naturally outliers in the data and multiple source models. The new theory is tested for the task of multiple motion estimation. Numerous experiments validate the theory and provide very competitive results.