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» Robustness of Classifiers to Changing Environments
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ICPR
2010
IEEE
13 years 5 months ago
Robust Foreground Object Segmentation via Adaptive Region-Based Background Modelling
We propose a region-based foreground object segmentation method capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds (as often...
Vikas Reddy, Conrad Sanderson, Brian C. Lovell
AIIA
2007
Springer
14 years 1 months ago
Robust Color-Based Skin Detection for an Interactive Robot
Detection of human skin in an arbitrary image is generally hard. Most color-based skin detection algorithms are based on a static color model of the skin. However, a static model c...
Alvise Lastra, Alberto Pretto, Stefano Tonello, Em...
ICMI
2004
Springer
281views Biometrics» more  ICMI 2004»
14 years 1 months ago
Articulatory features for robust visual speech recognition
Visual information has been shown to improve the performance of speech recognition systems in noisy acoustic environments. However, most audio-visual speech recognizers rely on a ...
Kate Saenko, Trevor Darrell, James R. Glass
EXACT
2009
13 years 5 months ago
Explaining a Result to the End-User: A Geometric Approach for Classification Problems
This paper addresses the issue of the explanation of the result given to the end-user by a classifier, when it is used as a decision support system. We consider machine learning cl...
Isabelle Alvarez, Sophie Martin
ECCV
2006
Springer
14 years 9 months ago
Robust Attentive Behavior Detection by Non-linear Head Pose Embedding and Estimation
We present a new scheme to robustly detect a type of human attentive behavior, which we call frequent change in focus of attention (FCFA), from video sequences. FCFA behavior can b...
Nan Hu, Weimin Huang, Surendra Ranganath