Analog neural networks for focal-plane image processing

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Abstract
Autonomous systems have to overcome two major problems in interpreting the data collected by sensors. These problems are due to the fact that multiple real world scenes can produce a given intensity distribution and that sensor data is often corrupted by noise. These problems can be solved by imposing certain a priori knowledge about the world to arrive at a unique solution. This reduces early vision problems to constrained optimization problems, which can be conveniently formulated as energy minimization problems in the framework of Regularization Theory. These energy functions can be implemented in analog hardware. This feature makes this approach very attractive for real time autonomous systems. We are presently developing algorithms and analog CMOS chips based on this approach for focal plane image processing.

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