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Optical Engineering

Texture metric that predicts target detection performance
Author(s): Joanne B. Culpepper
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Paper Abstract

Two texture metrics based on gray level co‐occurrence error (GLCE) are used to predict probability of detection and mean search time. The two texture metrics are local clutter metrics and are based on the statistics of GLCE probability distributions. The degree of correlation between various clutter metrics and the target detection performance of the nine military vehicles in complex natural scenes found in the Search_2 dataset are presented. Comparison is also made between four other common clutter metrics found in the literature: root sum of squares, Doyle, statistical variance, and target structure similarity. The experimental results show that the GLCE energy metric is a better predictor of target detection performance when searching for targets in natural scenes than the other clutter metrics studied.

Paper Details

Date Published: 8 December 2015
PDF: 13 pages
Opt. Eng. 54(12) 123101 doi: 10.1117/1.OE.54.12.123101
Published in: Optical Engineering Volume 54, Issue 12
Show Author Affiliations
Joanne B. Culpepper, Defence Science and Technology Group (Australia)


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