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"
}