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Classifiers for Matrix Normal Images: Derivation and Testing

  • Ewaryst RafajłowiczEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

We propose a modified classifier that is based on the maximum a posteriori probability principle that is applied to images having the matrix normal distributions. These distributions have a special covariance structure, which is interpretable and easier to estimate than general covariance matrices. The modification is applicable when the estimated covariance matrices are still not well-conditioned. The proposed classifier is tested on synthetic images and on images of gas burner flames. The results of comparisons with other classifiers are also provided.

Keywords

Matrix normal distribution Bayesian classifier Classification of flames 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of ElectronicsWrocław University of Science and TechnologyWrocławPoland

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