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Object selection in computer vision: From multi-thresholding to percolation based scene representation

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dc.contributor.author Volkov V.Y.
dc.contributor.author Bogachev M.I.
dc.contributor.author Kayumov A.R.
dc.date.accessioned 2021-02-25T06:47:19Z
dc.date.available 2021-02-25T06:47:19Z
dc.date.issued 2020
dc.identifier.issn 1868-4394
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/161021
dc.description.abstract © Springer Nature Switzerland AG 2020. We consider several approaches to the multi-threshold analysis of monochromatic images and consequent interpretation of its results in computer vision systems. The key aspect of our analysis is that it is based on a complete scene reconstruction leading to the object based scene representation inspired by principles from percolation theory. As a generalization of the conventional image segmentation, the proposed reconstruction leads to a multi-scale hierarchy of objects, thus allowing embedded objects to be represented at different scales. Using this reconstruction, we next suggest a direct approach to the object selection as a subset of the reconstructed scene based on a posteriori information obtained by multi-thresholding at the cost of the algorithm performance. We consider several geometric invariants as selection algorithm variables and validate our approach explicitly using prominent examples of synthetic models, remote sensing images, and microscopic data of biological samples.
dc.relation.ispartofseries Intelligent Systems Reference Library
dc.subject Adaptive thresholding
dc.subject CLSM imaging
dc.subject Hierarchical structure
dc.subject Multi-threshold analysis
dc.subject Object selection
dc.subject Percolation
dc.subject Z-stack
dc.title Object selection in computer vision: From multi-thresholding to percolation based scene representation
dc.type Chapter
dc.relation.ispartofseries-volume 175
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 161
dc.source.id SCOPUS18684394-2020-175-SID85087171876


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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