Possibilistic image processing

Mark J. Wierman

doi:10.1117/12.57081

1 February 1992 Possibilistic image processing

Mark J. Wierman

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57081
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

There are many methods of processing a digitized image. Some are local, such as edge finding, and some are global, such as contrast enhancement. There are frequency domain methods (using the Fourier transform) and spatial domain methods that process the direct values. Among all these methods, the maximum entropy (ME) method claims to do the best job, though sometimes at a computationally high burden. Adaptive Kalman filters are claimed to be as good as ME and computationally more attractive. Outside the analytic world, neural networks and fuzzy set theory have been applied with remarkable results. This paper presents a new methodology based on possibility theory that is in some ways analogous to the ME method of probability theory.

Citation Download Citation

Mark J. Wierman "Possibilistic image processing", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57081

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