Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video object segmentation and tracking using region-based statistics
Image Communication
Journal of Mathematical Imaging and Vision
Multi-Reference Shape Priors for Active Contours
International Journal of Computer Vision
Online visual tracking with histograms and articulating blocks
Computer Vision and Image Understanding
Particle filtering with dynamic shape priors
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
An integrated model for accurate shape alignment
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A unified framework for segmentation-assisted image registration
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Multiple object tracking via prediction and filtering with a sobolev-type metric on curves
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
A robust medical image segmentation method using KL distance and local neighborhood information
Computers in Biology and Medicine
Adaptive diffusion flow active contours for image segmentation
Computer Vision and Image Understanding
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We present a novel method for tracking objects by combiningdensity matching with shape priors. Density matchingis a tracking method which operates by maximizing theBhattacharyya similarity measure between the photometricdistribution from an estimated image region and a modelphotometric distribution. Such trackers can be expressed asPDE-based curve evolutions, which can be implemented usinglevel sets. Shape priors can be combined with this level-setimplementation of density matching by representing theshape priors as a series of level sets; a variational approachallows for a natural, parametrization-independentshape term to be derived. Experimental results on real imagesequences are shown.