A neural network model generating invariance for visual distance

Abstract

We present a neural network mechanism allowing for distance-invariant recognition of visual objects. The term distance invariance refers to the toleration of changes in retinal image size that are due to varying view distances, as opposed to varying real-world object size. We propose a biologically plausible network model, based on the recently demonstrated spike-rate modulations of large numbers of neurons in striate and extra-striate visual cortex by viewing distance. In this context, we introduce the concept of distance complex cells. Our model demonstrates the capability of distance-invariant object recognition, and of resolving conflicts that other approaches to size-invariant recognition do not address. © Springer-Verlag Berlin Heidelberg 2002.