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Proceedings Paper

Robust volumetric change detection using mutual information with 3D fractals
Author(s): Mark Rahmes; Morris Akbari; Ronda Henning; John Pokorny
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Paper Abstract

We discuss a robust method for quantifying change of multi-temporal remote sensing point data in the presence of affine registration errors. Three dimensional image processing algorithms can be used to extract and model an electronic module, consisting of a self-contained assembly of electronic components and circuitry, using an ultrasound scanning sensor. Mutual information (MI) is an effective measure of change. We propose a multi-resolution 3D fractal algorithm which is a novel extension to MI or regional mutual information (RMI). Our method is called fractal mutual information (FMI). This extension efficiently takes neighborhood fractal patterns of corresponding voxels (3D pixels) into account. The goal of this system is to quantify the change in a module due to tampering and provide a method for quantitative and qualitative change detection and analysis.

Paper Details

Date Published: 18 June 2014
PDF: 10 pages
Proc. SPIE 9097, Cyber Sensing 2014, 90970J (18 June 2014); doi: 10.1117/12.2047291
Show Author Affiliations
Mark Rahmes, Harris Corp. (United States)
Morris Akbari, Harris Corp. (United States)
Ronda Henning, Harris Corp. (United States)
John Pokorny, Harris Corp. (United States)

Published in SPIE Proceedings Vol. 9097:
Cyber Sensing 2014
Igor V. Ternovskiy; Peter Chin, Editor(s)

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