KFU. Card publication. Object Selection in Computer Vision: From Multi-thresholding to Percolation Based Scene Representation

KAZAN
FEDERAL UNIVERSITY

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OBJECT SELECTION IN COMPUTER VISION: FROM MULTI-THRESHOLDING TO PERCOLATION BASED SCENE REPRESENTATION

Form of presentation

International monographs

Year of publication

2020

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Authors, employees of KFU

Bogachev Mikhail Igorevich, author

Kayumov Ayrat Rashitovich, author

Bibliographic description in the original language

Volkov V.Y., Bogachev M.I., Kayumov A.R. Object Selection in Computer Vision: From Multi-thresholding to Percolation Based Scene Representation. In: Margarita N. Favorskaya and Lakhmi C. Jain (Eds): Computer Vision in Advanced Control Systems-5: Advanced Decisions in Technical and Medical Applications. Intelligent Systems Reference Library, vol 175. Basel: Springer Nature Switzerland AG, 2020.

Annotation

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.

Keywords

Object selection, Multi-threshold analysis, Percolation, Hierarchical structure, Adaptive thresholding, CLSM, imaging Z-stack

URL

https://link.springer.com/chapter/10.1007/978-3-030-33795-7_6

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=220523&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Bogachev Mikhail Igorevich	ru_RU
dc.contributor.author	Kayumov Ayrat Rashitovich	ru_RU
dc.date.accessioned	2020-01-01T00:00:00Z	ru_RU
dc.date.available	2020-01-01T00:00:00Z	ru_RU
dc.date.issued	2020	ru_RU
dc.identifier.citation	Volkov V.Y., Bogachev M.I., Kayumov A.R. Object Selection in Computer Vision: From Multi-thresholding to Percolation Based Scene Representation. In: Margarita N. Favorskaya and Lakhmi C. Jain (Eds): Computer Vision in Advanced Control Systems-5: Advanced Decisions in Technical and Medical Applications. Intelligent Systems Reference Library, vol 175. Basel: Springer Nature Switzerland AG, 2020.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=220523&p_lang=2	ru_RU
dc.description.abstract	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.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Object selection	ru_RU
dc.subject	Multi-threshold analysis	ru_RU
dc.subject	Percolation	ru_RU
dc.subject	Hierarchical structure	ru_RU
dc.subject	Adaptive thresholding	ru_RU
dc.subject	CLSM	ru_RU
dc.subject	imaging Z-stack	ru_RU
dc.title	Object Selection in Computer Vision: From Multi-thresholding to Percolation Based Scene Representation	ru_RU
dc.type	International monographs	ru_RU