<title>Autonomous visual discovery</title>

Abstract

This paper describes a prototype visual discovery algorithm that is designed to identify regions of an image that differ significantly from the local background. Image regions are projected into a visually-relevant subspace using a set of multi-orientation, multi-scale Gabor filters that model the receptive field properties of simple cells in the human visual cortex. Within this filter response subspace, deviant areas are identified through an adaptive statistical test that compares the filter-space description of a region against a model derived from the local background. Deviant regions are then spatially agglomerated and grouped across scale. Experimentation on a variety of archived imagery collected by JPL spacecraft and ground-based telescopes shows that the algorithm is able to autonomously 're-discover' a number of important geological objects such as impact craters, volcanoes, sand dunes, and ice geysers that are known to be of interest to planetary scientists.