Modeling Object Recognition
Teaching a computer how to recognize objects has been exceedingly difficult because a computer model has two paradoxical goals. It needs to create a representation for a particular object that is very specific, such as a horse as opposed to a cow or a unicorn. At the same time the representation must be sufficiently "invariant" so as to discard meaningless changes in pose, illumination, size, position, and many other variations in appearances.
Even a child's brain handles these contradictory tasks easily in rapid object recognition. Pixel-like information enters from the retina and passes in a fast feed-forward, bottom-up sweep through the hierarchical architecture of the visual cortex. What makes the Poggio lab's model so innovative and powerful is that, computationally speaking, it mimics the brain's own hierarchy. Specifically, the "layers" within the model replicate the way neurons process input and output stimuli ?according to neural recordings in physiological labs. Like the brain, the model alternates several times between computations that help build an object representation that is increasingly invariant to changes in appearances of an object in the visual field and computations that help build an object representation that is increasingly complex and specific to a given object.
The model's success validates work in physiology labs that have measured the tuning properties of neurons throughout visual cortex. By necessity, most of those experiments are made with simplistic artificial stimuli, such as gratings, bars, and line drawings that bear little resemblance to real-world images. "We put together a system that mimics as closely as possible how cortical cells respond to simple stimuli like the ones that are used in the physiology lab," said Serre. "The fact that this system seems to work on realistic street scene images is a concept p
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Source:McGovern Institute for Brain Research