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ICML
2009
IEEE
16 years 7 months ago
Robust feature extraction via information theoretic learning
In this paper, we present a robust feature extraction framework based on informationtheoretic learning. Its formulated objective aims at simultaneously maximizing the Renyi's...
Xiaotong Yuan, Bao-Gang Hu
ICIP
2005
IEEE
16 years 12 days ago
Robust highlight extraction using multi-stream hidden Markov models for baseball video
This paper proposes a robust statistical framework to extract highlights from a baseball broadcast video. We applied multistream Hidden Markov Models (HMMs) to control the weights...
Nguyen Huu Bach, Koichi Shinoda, Sadaoki Furui
ICARCV
2002
IEEE
110views Robotics» more  ICARCV 2002»
15 years 11 months ago
A novel robust method for large numbers of gross errors
In computer vision tasks, it frequently happens that gross noise occupies the absolute majority of the data. Most robust estimators can tolerate no more than 50% gross errors. In ...
Hanzi Wang, David Suter
CVPR
2010
IEEE
15 years 11 months ago
On the design of robust classifiers for computer vision
The design of robust classifiers, which can contend with the noisy and outlier ridden datasets typical of computer vision, is studied. It is argued that such robustness requires l...
Hamed Masnadi-Shirazi, Nuno Vasconcelos, Vijay Mah...
ICASSP
2010
IEEE
15 years 7 months ago
Algorithms for robust linear regression by exploiting the connection to sparse signal recovery
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly ...
Yuzhe Jin, Bhaskar D. Rao