Texture metric that predicts target detection performance

Joanne B. Culpepper

doi:10.1117/1.OE.54.12.123101

8 December 2015 Texture metric that predicts target detection performance

Joanne B. Culpepper

Author Affiliations +

Optical Engineering, Vol. 54, Issue 12, 123101 (December 2015). https://doi.org/10.1117/1.OE.54.12.123101

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.

Citation Download Citation

Joanne B. Culpepper "Texture metric that predicts target detection performance," Optical Engineering 54(12), 123101 (8 December 2015). https://doi.org/10.1117/1.OE.54.12.123101

Published: 8 December 2015

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