Similarity Evaluation of 3D Gray Rock Image Using Pattern Density Classification Function

DOI

10.2991/mmsa-18.2018.55How to use a DOI?

Keywords

3D rock models; pattern density classification function; morphological similarity; texture similarity

Abstract

Aiming at the problem that the existing 3D core similarity evaluation methods cannot effectively evaluate gray core images, we proposed a similarity evaluation algorithm based on Pattern Density Classification Function (PDCF). First of all, the 3D template is used to extract the texture patterns of 3D core images, and then the pattern density classification function is formed with the extracted patterns by adopting K-means algorithm. An adaptive method is used to find out the appropriate K value. Finally, the pattern density classification function is used to measure the texture similarity between 3D gray rock models. In this paper, a comparative experiment of multiple groups of core images is carried out. Combined with the existing similarity evaluation algorithm for the binary image of 3D core, the 3D gray core model similarity characterization is realized from morphological and texture distribution.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaohai He
AU  - Zhengji Li
AU  - Qizhi Teng
AU  - Linbo Qing
AU  - Xiaohong Wu
PY  - 2018/03
DA  - 2018/03
TI  - Similarity Evaluation of 3D Gray Rock Image Using Pattern Density Classification Function
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 244
EP  - 248
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.55
DO  - 10.2991/mmsa-18.2018.55
ID  - He2018/03
ER  -