A neural network model of the primary visual cortex.

Many problems in modern computing require a visual component. That is to say, it is fairly common for applications to have a need to see their environments. These applications will typically employ techniques designed specifically to solve the particular task needed for the application, and have little or no relation to the human visual system. Humans generally do not have difficulty interpreting the world around us. When traveling through known environments, we can easily recognize particular walls, doors and other objects in our view. We are not confused by the huge number factors that can complicate an image. The generalization and robustness of the human system would provide a huge benefit to any system that requires more advanced vision than is capable with the ad-hoc methods developed previously. If the underlying principles that make the human visual system so powerful can be identified and implemented programmatically, then a machine could reap the benefits obtained by humans. The purpose of this thesis is to demonstrate that a visual system modeled after the human visual system will be robust and accurate enough to solve real world problems - and to be useful in a non-trivial application. By developing neural networks that directly model the most primitive image processing cells of the human visual system, a platform can be built on which advanced vision systems can be developed.