Statistical image algebra: a Bayesian approach

Jennifer L. Davidson; Noel A. C. Cressie

doi:10.1117/12.48387

1 October 1991 Statistical image algebra: a Bayesian approach

Jennifer L. Davidson, Noel A. C. Cressie

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48387
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

A mathematical structure used to express image processing transforms, the AFATL image algebra has proven itself useful in a wide variety of applications. The theoretical foundation for the image algebra includes many important constructs for handling a wide variety of image processing problems: questions relating to linear and nonlinear transforms, including decomposition techniques; mapping of transformations to computer architectures; neural networks; recursive transforms; and data manipulation on hexagonal arrays. However, statistical notions have been included only on a very elementary level and on a more sophisticated level in the literature. This paper presents an extension of the current image algebra that includes a Bayesian statistical approach. It is shown how images are modeled as random vectors, probability functions or mass functions are modeled as images, and conditional probability functions are modeled as templates. The remainder of the paper gives a brief discussion of the current image algebra, an example of the use of image algebra to express high-level image processing transforms, and the presentation of the statistical development of the image algebra.

Citation Download Citation

Jennifer L. Davidson and Noel A. C. Cressie "Statistical image algebra: a Bayesian approach", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48387

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