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We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of ...

Humans and other primates can rapidly categorize objects even when they are embedded in complex visual scenes (Thorpe et al., 1996; Fabre-Thorpe et al., 1998). Studies by Serre et al., 2007 have shown that the ability of ...

Apart from helping shed some light on human perceptual mechanisms, modeling visual attention has important applications in computer vision. It has been shown to be useful in priming object detection, pruning interest points, ...

We describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and a very large manually annotated video database used for training and testing the system. ...

The past four decades of research in visual neuroscience has generated a large and disparate body of literature on the role of attention [Itti et al., 2005]. Although several models have been developed to describe specific ...

In this paper, we introduce a novel set of features for robust object recognition, which exhibits outstanding performances on a variety ofobject categories while being capable of learning from only a fewtraining examples. ...

The human visual system can recognize several thousand object categories irrespective of their position and size. This combination of selectivity and invariance is built up gradually across several stages of visual processing. ...

One of the most striking feature of the cortex is its ability to wire itself. Understanding how the visual cortex wires up through development and how visual experience refines connections into adulthood is a key question ...