Publication detail
Detection of Edge Structures on Surface of Sedimentary Grains Acquired by Electron Microscope
KŘUPKA, A. ŘÍHA, K.
Original Title
Detection of Edge Structures on Surface of Sedimentary Grains Acquired by Electron Microscope
English Title
Detection of Edge Structures on Surface of Sedimentary Grains Acquired by Electron Microscope
Type
conference paper
Language
en
Original Abstract
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.
English abstract
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.
Keywords
Edge structure, co-occurrence matrix, logistic regression model, sedimentary grains, edge length
RIV year
2014
Released
09.07.2015
Location
Berlin, Germany
ISBN
978-1-4799-8497-8
Book
Proceedings of the 38th International Conference on Telecommunication and Signal Processing
Pages from
785
Pages to
788
Pages count
4
URL
Documents
BibTex
@inproceedings{BUT109404,
author="Aleš {Křupka} and Kamil {Říha}",
title="Detection of Edge Structures on Surface of Sedimentary Grains Acquired by Electron Microscope",
annote="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.",
booktitle="Proceedings of the 38th International Conference on Telecommunication and Signal Processing",
chapter="109404",
doi="10.1109/TSP.2015.7296373",
howpublished="online",
year="2015",
month="july",
pages="785--788",
type="conference paper"
}