New image processing software for analyzing object size-frequency distributions, geometry, orientation, and spatial distribution

Authors:
Ciarán Beggan;Christopher W. Hamilton
Affiliations:
Grant Institute, School of GeoSciences, University of Edinburgh, West Mains Road, Edinburgh EH9 3JW, UK;Hawai'i Institute of Geophysics and Planetology, University of Hawai'i, USA
Venue:
Computers & Geosciences
Year:
2010

Citing 3
Cited 2

Image Thresholding by Indicator Kriging

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recongnition and separation of discrete objects with in complex 3D voxelized structures

Computers & Geosciences - Special issue on three-dimensional reconstruction, modelling and visualization of geologic materials
Analysis of the vesicular structure of basalts

Computers & Geosciences

jPOR: An ImageJ macro to quantify total optical porosity from blue-stained thin sections

Computers & Geosciences
Using image analysis and ArcGIS® to improve automatic grain boundary detection and quantify geological images

Computers & Geosciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

Geological Image Analysis Software (GIAS) combines basic tools for calculating object area, abundance, radius, perimeter, eccentricity, orientation, and centroid location, with the first automated method for characterizing the aerial distribution of objects using sample-size-dependent nearest neighbor (NN) statistics. The NN analyses include tests for (1) Poisson, (2) Normalized Poisson, (3) Scavenged k=1, and (4) Scavenged k=2 NN distributions. GIAS is implemented in MATLAB with a Graphical User Interface (GUI) that is available as pre-parsed pseudocode for use with MATLAB, or as a stand-alone application that runs on Windows and Unix systems. GIAS can process raster data (e.g., satellite imagery, photomicrographs, etc.) and tables of object coordinates to characterize the size, geometry, orientation, and spatial organization of a wide range of geological features. This information expedites quantitative measurements of 2D object properties, provides criteria for validating the use of stereology to transform 2D object sections into 3D models, and establishes a standardized NN methodology that can be used to compare the results of different geospatial studies and identify objects using non-morphological parameters.