Machine vision applications of image invariants: real-time processing experiments

Paul Max Payton; Barry K. Haines; Kirk G. Smedley; Eamon B. Barrett

doi:10.1117/12.47968

1 April 1991 Machine vision applications of image invariants: real-time processing experiments

Paul Max Payton, Barry K. Haines, Kirk G. Smedley, Eamon B. Barrett

Proceedings Volume 1406, Image Understanding in the '90s: Building Systems that Work; (1991) https://doi.org/10.1117/12.47968
Event: Applied Imaging Pattern Recognition, 1990, McLean, VA, United States

Abstract

As part of on-going studies of automated techniques for object recognition in imagery, recent experiments in two and three dimensions have produced promising results. Newly developed methods that exploit projectively invariant relationships in imagery are able to recognize the same object in images that differ in tilt, scale and rotation. Automatically extracted corner points are used as the base features in simple two-dimensional objects, and patches of known gray-value are used in threedimensional terrain perspective views. In both cases, projective invariants are calculated and compared with a catalog of archetypal values, resulting in successful identification of the objects within experimental error.

Citation Download Citation

Paul Max Payton, Barry K. Haines, Kirk G. Smedley, and Eamon B. Barrett "Machine vision applications of image invariants: real-time processing experiments", Proc. SPIE 1406, Image Understanding in the '90s: Building Systems that Work, (1 April 1991); https://doi.org/10.1117/12.47968

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