Classifiers for Matrix Normal Images: Derivation and Testing
Conference paper
First Online:
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 flamesReferences
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