Nonparametric estimation of the number of classes with different average brightness in thermal images
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...
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| Format: | Article |
| Language: | English |
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Samara National Research University
2023-10-01
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| Series: | Компьютерная оптика |
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| Online Access: | https://www.computeroptics.ru/eng/KO/Annot/KO47-5/470516e.html |
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| author | A.N. Galyntich M.A. Raifeld |
| author_facet | A.N. Galyntich M.A. Raifeld |
| author_sort | A.N. Galyntich |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d415b79955d640cc813d795d7f82b63f |
| institution | Kabale University |
| issn | 0134-2452 2412-6179 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj-art-d415b79955d640cc813d795d7f82b63f2025-01-23T06:04:17ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792023-10-0147581682310.18287/2412-6179-CO-1284Nonparametric estimation of the number of classes with different average brightness in thermal imagesA.N. Galyntich0M.A. Raifeld1Branch of JSC "PO UOMZ Ural-SibNIIRS"; Novosibirsk State Technical University,Novosibirsk State Technical University,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.https://www.computeroptics.ru/eng/KO/Annot/KO47-5/470516e.htmlimage segmentationnonparametric algorithmrank histogrameigenvaluesgram-schmidt orthogonalizationprincipal component method |
| spellingShingle | A.N. Galyntich M.A. Raifeld Nonparametric estimation of the number of classes with different average brightness in thermal images Компьютерная оптика image segmentation nonparametric algorithm rank histogram eigenvalues gram-schmidt orthogonalization principal component method |
| title | Nonparametric estimation of the number of classes with different average brightness in thermal images |
| title_full | Nonparametric estimation of the number of classes with different average brightness in thermal images |
| title_fullStr | Nonparametric estimation of the number of classes with different average brightness in thermal images |
| title_full_unstemmed | Nonparametric estimation of the number of classes with different average brightness in thermal images |
| title_short | Nonparametric estimation of the number of classes with different average brightness in thermal images |
| title_sort | nonparametric estimation of the number of classes with different average brightness in thermal images |
| topic | image segmentation nonparametric algorithm rank histogram eigenvalues gram-schmidt orthogonalization principal component method |
| url | https://www.computeroptics.ru/eng/KO/Annot/KO47-5/470516e.html |
| work_keys_str_mv | AT angalyntich nonparametricestimationofthenumberofclasseswithdifferentaveragebrightnessinthermalimages AT maraifeld nonparametricestimationofthenumberofclasseswithdifferentaveragebrightnessinthermalimages |