Feature guided visual attention with topographic array processing and neural network-based classification

Biological systems are constantly engulfed in sensory input that must be processed. Attention has evolved to cut down on the magnitude of the input and enable the agent to analyze the most important parts of the information. This is especially true for the visual system where the appropriate field of view and scale must be determined. Our system receives a video flow with considerably higher resolution than the resolution of the cellular neural net based visual microprocessor that computes the topographic features of the input. This process requires a dynamic positioning of the processing window in the video flow. We have developed a fast attention and selection algorithm that allows the system to choose the field of view and scale (zoom) level for the next frame based on the features computed from the current frame and the output of the ART or NNC-based classifiers. The algorithmic framework and hardware architecture of the system are presented along with experimental chip results for several video flows recorded in flying vehicles.