| Форма представления | Зарубежные монографии |
| Год публикации | 2020 |
| Язык | английский |
|
Богачев Михаил Игоревич, автор
Каюмов Айрат Рашитович, автор
|
| Библиографическое описание на языке оригинала |
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. |
| Аннотация |
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.
|
| Ключевые слова |
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 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=220523 |
Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Богачев Михаил Игоревич |
ru_RU |
| dc.contributor.author |
Каюмов Айрат Рашитович |
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/?p_id=220523 |
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 |
Зарубежные монографии |
ru_RU |
|
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