Multiresolution attention and associative memory systems for time-varying imagery

This paper describes a vision system that integrates attention and recognition systems using active (where and what to sense) and selective (how to control the stream of computation) perception. Both time-varying imagery input and recognition memory are organized as pyramids and a uniform indexing and classification interface is established. The tools discussed in this paper include the use of: (1) scale-space representation using wavelet pyramids; (2) hierarchical, localized, and robust indexing and classification of the feature models using distributed associative memory; and (3) adaptive saccade and zoom strategies guided by saliency in determining focus of attention to locate and identify target objects.