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Nonparametric estimation of the number of classes with different average brightness in thermal images
A.N. Galyntich 1,2, M.A. Raifeld 2

1 Branch of JSC "PO UOMZ Ural-SibNIIRS", 630082, Russia, Novosibirsk, st. D. Kovalchuk 179/2;
2 Novosibirsk State Technical University, 630073, Russia, Novosibirsk, K. Marksa Ave. 20

 PDF, 1210 kB

DOI: 10.18287/2412-6179-CO-1284

Pages: 816-823.

Full text of article: Russian language.

Abstract:
When there is no information about the number of brightness classes, synthesizing algorithms for automatic image threshold segmentation involves a problem of determining the number of thresholds. The solution to the problem of estimating the number of classes in an image can be based on representing its distribution as a mixture of distributions of brightness classes when priori probabilities are unknown, or estimating the number of histogram modes. At the same time, it is known that the mixture splitting problem has a solution only for certain types of distributions and the histogram modes are not always distinguishable. In the general case, when the distributions of brightness classes are unknown, there are difficulties in applying these methods. The article proposes a non-parametric approach to determining the number of classes that differ in average brightness, based on rank histograms and using the property of local spatial grouping of elements of each brightness class in the image.

Keywords:
image segmentation, nonparametric algorithm, rank histogram, eigenvalues, Gram-Schmidt orthogonalization, principal component method.

Citation:
Galyntich AN, Raifeld MA. Nonparametric estimation of the number of classes with different average brightness in thermal images. Computer Optics 2023; 47(5): 816-823. DOI: 10.18287/2412-6179-CO-1284.

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