language: en

CAR-TR-858 N00014-95-1-0521 June 1997

Information-Conserving Object Recognition

Margrit Betke and Nicholas C. Makris :
Computer Vision Laboratory Center for Automation Research University of Maryland
College Park, MD 20742-3275

Abstract The problem of recognizing objects imaged in complex real-world scenes is examined from a parametric perspective using the theory of statistical estimation. A scalar measure of an object's complexity, which is invariant under affine transformation and changes in image noise level, is extracted from the object's Fisher information. The volume of Fisher information is shown to provide an overall statistical measure of the object's recognizability in a particular image, while the complexity provides an intrinsically physical measure that characterizes the object in any image. An information-conserving method is then developed for recognizing an object imaged in a complex scene. Here the term "information-conserving" means that the method uses all the measured data pertinent to the object's recognizability, attains the theoretical lower bound on estimation error for any unbiased estimate of the parameter vector describing the object, and therefore is statistically optimal. This method is then successfully applied to finding objects imaged in thousands of complex real-world scenes.

[The support of the Office of Naval Research under Contract N00014-95-1-0521 is gratefully acknowledged. Author's new address (starting September 1997):]

Department of Computer Science, Boston College,
Fulton Hall, Chestnut Hill, MA 02167.
Email: betke@cs.bc.edu

[N. C. Makris was with the Naval Research Laboratory, Washington, DC 20375. His new address is]

Department of Ocean Engineering, Massachusetts Institute of Technology,
Cambridge, MA 02138.