Information Theory Approach to Crystallographic Symmetry Classifications of Noisy 2D Periodic Images

Authors

Peter Moeck, Portland State UniversityFollow

Published In

2018 IEEE 13th Nanotechnology Materials and Devices Conference (NMDC)

Document Type

Citation

Publication Date

1-2019

Abstract

An information theory inspired approach, i.e. utilization of Geometric Akaike Information Criteria (G-AICs), individual Akaike weights, and products of Akaike weights, allows for objective crystallographic symmetry classification of images that are more or less periodic in two dimensions (2D). The combined usage of G-AICs for Bravais lattice types and plane symmetry groups should enable successful classifications even in the presence of severe pseudo-symmetry challenges.

Description

© Copyright 2019 IEEE - All rights reserved.

Locate the Document

https://doi.org/10.1109/NMDC.2018.8605865

DOI

10.1109/NMDC.2018.8605865

Persistent Identifier

https://archives.pdx.edu/ds/psu/29052

Citation Details

P. Moeck, "Information Theory Approach to Crystallographic Symmetry Classifications of Noisy 2D Periodic Images," 2018 IEEE 13th Nanotechnology Materials and Devices Conference (NMDC), Portland, OR, 2018, pp. 1-4.