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Detail publikace
KŘUPKA, A. ŘÍHA, K.
Originální název
Detection of Edge Structures on Surface of Sedimentary Grains Acquired by Electron Microscope
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper presents a method for edge detection on the surface of sedimentary grains that were acquired by an electron microscope. Local grain parts are described by textural co-occurrence features. Edges are then detected by classification of co-occurrence features corresponding to particular parts of image. For this classification, a logistic regression model is used. The precision and recall values of the cross-validated model are 82% and 77% respectively. Further, a measure that quantifies a maximal edge length detected on a grain is proposed. The purpose of this measure is to provide a high-level feature for comparing different grain sets. To evaluate a usability of the measure, the measure is computed for sets of grains of different geomorphological geneses and the differences are compared. Because the results showed a specific measure range for some geneses, the proposed edge detection method can be considered as useful for description of sedimentary grains.
Klíčová slova
Edge structure, co-occurrence matrix, logistic regression model, sedimentary grains, edge length
Autoři
KŘUPKA, A.; ŘÍHA, K.
Rok RIV
2014
Vydáno
9. 7. 2015
Místo
Berlin, Germany
ISBN
978-1-4799-8497-8
Kniha
Proceedings of the 38th International Conference on Telecommunication and Signal Processing
Strany od
785
Strany do
788
Strany počet
4
URL
https://ieeexplore.ieee.org/document/7296373
BibTex
@inproceedings{BUT109404, author="Aleš {Křupka} and Kamil {Říha}", title="Detection of Edge Structures on Surface of Sedimentary Grains Acquired by Electron Microscope", booktitle="Proceedings of the 38th International Conference on Telecommunication and Signal Processing", year="2015", pages="785--788", address="Berlin, Germany", doi="10.1109/TSP.2015.7296373", isbn="978-1-4799-8497-8", url="https://ieeexplore.ieee.org/document/7296373" }