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ICCV
2009
IEEE
2061views Computer Vision» more  ICCV 2009»
16 years 5 months ago
Background Subtraction for Freely Moving Cameras
Background subtraction algorithms define the background as parts of a scene that are at rest. Traditionally, these algorithms assume a stationary camera, and identify moving obj...
Yaser Sheikh, Omar Javed, Takeo Kanade
CVPR
2000
IEEE
16 years 2 months ago
Representation and Recognition of Complex Human Motion
The quest for a vision system capable of representing and recognizing arbitrary motions benefits from a low dimensional, non-specific representation of flow fields, to be used in ...
Jesse Hoey, James J. Little
110
Voted
ECCV
2008
Springer
16 years 2 months ago
Compressive Sensing for Background Subtraction
Abstract. Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reco...
Volkan Cevher, Aswin C. Sankaranarayanan, Marco F....
79
Voted
ISBI
2009
IEEE
15 years 7 months ago
Improving M/EEG Source Localization with an Inter-Condition Sparse Prior
The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to set priors on the solution. Most common priors are based on a convenient ℓ2 norm....
Alexandre Gramfort, Matthieu Kowalski
ICPR
2006
IEEE
16 years 1 months ago
Convex Quadratic Programming for Object Localization
We set out an object localization scheme based on a convex programming matching method. The proposed approach is designed to match general objects, especially objects with very li...
Hao Jiang, Mark S. Drew, Ze-Nian Li