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ICCV
2009
IEEE
2061views Computer Vision» more  ICCV 2009»
16 years 4 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 1 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
98
Voted
ECCV
2008
Springer
16 years 28 days 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....
ISBI
2009
IEEE
15 years 5 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 4 days 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