A Neural Network Model Generating Invariance For Visual Distance

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.