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» Pearson-based mixture model for color object tracking
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VMV
2008
116views Visualization» more  VMV 2008»
13 years 8 months ago
A GPU-accelerated particle filter with pixel-level likelihood
We present in this paper a GPU-accelerated particle filter based on pixel-level segmentation and matching, for real-time object tracking. The proposed method achieves real-time pe...
Claus Lenz, Giorgio Panin, Alois Knoll
JMM2
2007
212views more  JMM2 2007»
13 years 6 months ago
Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking
— This paper makes new contributions in motion detection, object segmentation and trajectory estimation to create a successful object tracking system. A new efficient motion det...
Filiz Bunyak, Kannappan Palaniappan, Sumit Kumar N...
ICPR
2008
IEEE
14 years 1 months ago
Adaptive semantic Bayesian framework for image attention
Image attention is the basic technique for many computer vision applications. In this paper, we propose an adaptive Bayesian framework to detect the image attention in color image...
Wei Zhang, Q. M. Jonathan Wu, Guanghui Wang
PAMI
2007
155views more  PAMI 2007»
13 years 6 months ago
Localization of Shapes Using Statistical Models and Stochastic Optimization
—In this paper, we present a new model for deformations of shapes. A pseudolikelihood is based on the statistical distribution of the gradient vector field of the gray level. The...
François Destrempes, Max Mignotte, Jean-Fra...
ICCV
2009
IEEE
14 years 11 months ago
Adaptive Fragments-Based Tracking of Non-Rigid Objects Using Level Sets
We present an approach to visual tracking based on dividing a target into multiple regions, or fragments. The target is represented by a Gaussian mixture model in a joint feature...
Prakash Chockalingam, Nalin Pradeep