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ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
14 years 2 months ago
Variable Number of "Informative" Particles for Object Tracking
Particle filter is a sequential Monte Carlo method for object tracking in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on t...
Yu Huang, Joan Llach
IJRR
2006
120views more  IJRR 2006»
13 years 7 months ago
Vibration Estimation of Flexible Space Structures using Range Imaging Sensors
Future space applications will require robotic systems to assemble, inspect, and maintain large space structures in orbit. For effective planning and control, robots will need to ...
Matthew D. Lichter, Hiroshi Ueno, Steven Dubowsky
FLAIRS
2008
13 years 10 months ago
On Using SVM and Kolmogorov Complexity for Spam Filtering
As a side effect of e-marketing strategy the number of spam e-mails is rocketing, the time and cost needed to deal with spam as well. Spam filtering is one of the most difficult t...
Sihem Belabbes, Gilles Richard
CGF
2000
97views more  CGF 2000»
13 years 7 months ago
Motion Balance Filtering
This paper presents a new technique called motion balance filtering, which corrects an unbalanced motion to a balanced one while preserving the original motion characteristics as ...
Seyoon Tak, Oh-Young Song, Hyeong-Seok Ko
IWNAS
2006
IEEE
14 years 1 months ago
Comparisons of Three Kalman Filter Tracking Algorithms in Sensor Network
This paper compares extended Kalman filters with the P, PV and PVA dynamics models for object tracking in wireless network. Experiments shows that PVA achieves the best and P per...
Yifeng Zhu, Ali Shareef