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» Selecting What Is Important: Training Visual Attention
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ICRA
2007
IEEE
224views Robotics» more  ICRA 2007»
14 years 1 months ago
Visual Categorization Robust to Large Intra-Class Variations using Entropy-guided Codebook
Abstract— Categorizing visual elements is fundamentally important for autonomous mobile robots to get intelligence such as new object acquisition and topological place classific...
Sungho Kim, In-So Kweon, Chil-Woo Lee
CVPR
2006
IEEE
14 years 9 months ago
Joint Boosting Feature Selection for Robust Face Recognition
A fundamental challenge in face recognition lies in determining what facial features are important for the identification of faces. In this paper, a novel face recognition framewo...
Rong Xiao, Wu-Jun Li, Yuandong Tian, Xiaoou Tang
ICCV
1995
IEEE
13 years 11 months ago
Task-Oriented Generation of Visual Sensing Strategies
This paper describes a method of systematically generating visual sensing strategies based on knowledge of the assembly task to be performed. Since visual sensing is usually perfo...
Jun Miura, Katsushi Ikeuchi
IJVR
2007
123views more  IJVR 2007»
13 years 7 months ago
There-Reality: Selective Rendering in High Fidelity Virtual Environments
—There-reality environments are those virtual environments which evoke the same perceptual response from a viewer as if they were actually present, or there, in the real scene be...
Alan Chalmers, Kurt Debattista, Georgia Mastoropou...
CVPR
2011
IEEE
13 years 4 months ago
Saliency Estimation Using a Non-Parametric Low-Level Vision Model
Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to constru...
Naila Murray, Maria Vanrell, Xavier Otazu, C. Alej...