Evaluation of human detection performance of targets embedded in natural and enhanced infrared images using image metrics

G. Aviram, S. R. Rotman

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

The evaluation of human detection performance of targets embedded in natural IR images is uniquely extended to deal with enhanced images. Various image metrics designed to describe global image clutter and local target-to-background contrast are presented. Agreement between experimental results, as obtained for both natural and enhanced IR images and image metrics, is evaluated by correlating the metric values with target detection results obtained from two psycho-physical experiments, which were designed to record and measure human target detection performance and image quality judgments. Positive correlation values are generally obtained between the metric values and both the target detection (rate and probability) performance and the image quality scale values. More specifically, the local contrast metric is found to be the most suitable to the data recorded from the quality judgments experiment, while the global clutter metric is found to be the most suitable to the data recorded from the detection performance experiments. Further analysis of the relationship between the image metrics and the experimental results yield empirical classification thresholds that can be used to evaluate and predict human detection performance in similar cases. A method to evaluate the classification efficiency, is introduced and used to reconfirm the metrics appropriateness to describe detection performance and quality judgments of targets embedded in both natural and enhanced images.

Original languageEnglish
Pages (from-to)885-896
Number of pages12
JournalOptical Engineering
Volume39
Issue number4
DOIs
StatePublished - 1 Apr 2000

Fingerprint

Dive into the research topics of 'Evaluation of human detection performance of targets embedded in natural and enhanced infrared images using image metrics'. Together they form a unique fingerprint.

Cite this