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BILDMED
2009
251views Algorithms» more  BILDMED 2009»
13 years 8 months ago
Probabilistic Tracking and Model-Based Segmentation of 3D Tubular Structures
Abstract. We introduce a new approach for tracking-based segmentation of 3D tubular structures. The approach is based on a novel combination of a 3D cylindrical intensity model and...
Stefan Wörz, William J. Godinez, Karl Rohr
ICPR
2004
IEEE
14 years 8 months ago
Probabilistic Object Tracking Using Multiple Features
We present a generic tracker which can handle a variety of different objects. For this purpose, groups of low-level features like interest points, edges, homogeneous and textured ...
David Serby, Esther Koller-Meier, Luc J. Van Gool
CVPR
2004
IEEE
14 years 9 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
TCSV
2010
13 years 2 months ago
Object Tracking in Structured Environments for Video Surveillance Applications
Abstract--We present a novel tracking method for effectively tracking objects in structured environments. The tracking method finds applications in security surveillance, traffic m...
Junda Zhu, Yuanwei Lao, Yuan F. Zheng
TIP
2010
141views more  TIP 2010»
13 years 2 months ago
Efficient Particle Filtering via Sparse Kernel Density Estimation
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
Amit Banerjee, Philippe Burlina