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» Robustness of Classifiers to Changing Environments
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AVSS
2006
IEEE
13 years 11 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
ISVC
2007
Springer
14 years 2 months ago
Automated Scene-Specific Selection of Feature Detectors for 3D Face Reconstruction
In comparison with 2D face images, 3D face models have the advantage of being illumination and pose invariant, which provides improved capability of handling changing environments ...
Yi Yao, Sreenivas R. Sukumar, Besma R. Abidi, Davi...
DATAMINE
2008
143views more  DATAMINE 2008»
13 years 8 months ago
Automatically countering imbalance and its empirical relationship to cost
Learning from imbalanced datasets presents a convoluted problem both from the modeling and cost standpoints. In particular, when a class is of great interest but occurs relatively...
Nitesh V. Chawla, David A. Cieslak, Lawrence O. Ha...
IROS
2006
IEEE
528views Robotics» more  IROS 2006»
14 years 1 months ago
Dynamic Path Planning for a 7-DOF Robot Arm
— We present an on-line, robust, and efficient path planner for the redundant Mitsubishi PA-10 arm with 7 degrees of freedom (DOF) in non-stationary environments. Because of the...
Stefan Klanke, Dmitry V. Lebedev, Robert Haschke, ...
JMM2
2007
183views more  JMM2 2007»
13 years 7 months ago
A Chaos Theoretic Analysis of Motion and Illumination in Video Sequences
— Accurate and robust image motion detection has been of substantial interest in the image processing and computer vision communities. Unfortunately, no single motion detection a...
Michael E. Farmer