THE WHAT-AND-WHERE FILTER - A SPATIAL-MAPPING NEURAL-NETWORK FOR OBJECT RECOGNITION AND IMAGE UNDERSTANDING

The What-and-Where filter forms part of a neural network architecture for spatial mapping, object recognition, and image understanding. The Where filter responds to an image figure that has been separated from its background. It generates a spatial map whose cell activations simu ltaneously represent the position, orientation, and size of all the fi gures in a scene (where they are). This spatial map may be used to dir ect spatially localized attention to these image features. A multiscal e array of oriented detectors, followed by competitive and interpolati ve interactions between position, orientation, and size scales, is use d to define the Where filter. This analysis discloses several issues t hat need to be dealt with by a spatial mapping system that is based up on oriented filters, such as the role of cliff filters with and withou t normalization, the double peak problem of maximum orientation across size scale, and the different self-similar interpolation properties a cross orientation than across size scale, Several computationally effi cient Where filters are proposed. The Where filter may be used for par allel transformation of multiple image figures into invariant represen tations that are insensitive to the figures' original position, orient ation, and size, These invariant figural representations form part of a system devoted to attentive object learning and recognition (what it is). Unlike some alternative models where serial search for a target occurs, a What and Where representation can be used to rapidly search in parallel for a desired target in a scene, Such a representation can also be used to learn multidimensional representations of objects and their spatial relationships for purposes of image understanding, The What-and-Where filter is inspired by neurobiological data showing that a Where processing stream in the cerebral cortex is used for attentiv e spatial localization and orientation, whereas a What processing stre am is used for attentive object learning and recognition. (C) 1998 Aca demic Press.