Seth Cameron

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This article describes a self-organizing neural network architecture that transforms optic flow and eye position information into representations of heading, scene depth, and moving object locations. These representations are used to navigate reactively in simulations involving obstacle avoidance and pursuit of a moving target. The network's weights are(More)
The identification of key features (e.g. organs and tumours) in medical scans (CT, MRI, etc.) is a vital first step in many other image analysis applications, but it is by no means easy to identify such features automatically. Using statistical properties of image regions alone, it is not always possible to distinguish between different features with(More)
This paper describes a self-organizing neural network architecture that transforms optic flow information into representations of heading, scene depth, and moving object locations. These representations are used to reactively navigate in simulations involving obstacle avoidance and pursuit of a moving target. The network's weights are trained during an(More)
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