A Frequency Matching Method for Generation of a Priori Sample Models from Training Images | Zendy

Katrine Lange | Zendy; Knud Skou Cordua | Zendy; Jan Frydendall | Zendy; Thomas Mejer Hansen | Zendy; Klaus Mosegaard | Zendy

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A Frequency Matching Method for Generation of a Priori Sample Models from Training Images

Author(s) -

Katrine Lange,

Knud Skou Cordua,

Jan Frydendall,

Thomas Mejer Hansen,

Klaus Mosegaard

Publication year - 2011

Publication title -

technical university of denmark, dtu orbit (technical university of denmark, dtu)

Language(s) - English

Resource type - Reports

DOI - 10.5242/iamg.2011.0262

Subject(s) - a priori and a posteriori , matching (statistics) , sample (material) , artificial intelligence , training (meteorology) , computer science , pattern recognition (psychology) , training set , computer vision , mathematics , statistics , geography , chromatography , chemistry , philosophy , epistemology , meteorology

This paper presents a Frequency Matching Method (FMM) for generation of a priori sample models based on training images and illustrates its use by an example. In geostatistics, training images are used to represent a priori knowledge or expectations of models, and the FMM can be used to generate new images that share the same multi-point statistics as a given training image. The FMM proceeds by iteratively updating voxel values of an image until the frequency of patterns in the image matches the frequency of patterns in the training image; making the resulting image statistically indistinguishable from the training image. 1. Background

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