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.
Finding "anomalies" in an arbitrary image
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 2 ; 516-523 vol.2
2001-01-01
1091589 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Finding "Anomalies" in an Arbitrary Image
British Library Conference Proceedings | 2001
|British Library Conference Proceedings | 2003
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