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» Linear vs. Nonlinear Feature Combination for Saliency Comput...
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DAGM
2006
Springer
13 years 8 months ago
Linear vs. Nonlinear Feature Combination for Saliency Computation: A Comparison with Human Vision
In the heart of the computer model of visual attention, an interest or saliency map is derived from an input image in a process that encompasses several data combination steps. Whi...
Nabil Ouerhani, Alexandre Bur, Heinz Hügli
NIPS
2007
13 years 6 months ago
The discriminant center-surround hypothesis for bottom-up saliency
The classical hypothesis, that bottom-up saliency is a center-surround process, is combined with a more recent hypothesis that all saliency decisions are optimal in a decision-the...
Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos
ACCV
2009
Springer
14 years 3 months ago
Estimating Human Pose from Occluded Images
We address the problem of recovering 3D human pose from single 2D images, in which the pose estimation problem is formulated as a direct nonlinear regression from image observation...
Jia-Bin Huang and Ming-Hsuan Yang
ICCV
2009
IEEE
14 years 9 months ago
Multiple Kernels for Object Detection
Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image subwindows....
Andrea Vedaldi, Varun Gulshan, Manik Varma, Andrew...