Finding "anomalies" in an arbitrary image

Abstract

A fast and general method to extract "anomalies" in an arbitrary image is proposed. The basic idea is to compute a probability density for sub-regions in an image, conditioned upon the areas surrounding the sub-regions. Linear estimation and Independent Component Analysis (ICA) are combined to obtain the probability estimates. Pseudo non-parametric correlation is used to group sets of similar surrounding patterns, from which a probability for the occurrence of a given sub-region is derived. A carefully designed multi-dimensional histogram, based on compressed vector representations, enables efficient and high-resolution extraction of anomalies from the image. Our current (unoptimized) implementation performs anomaly extraction in about 30 seconds for a 640/spl times/480 image using a 700 MHz PC. Experimental results are included that demonstrate the performance of the proposed method.