TY - GEN
T1 - Modeling human false target detection decision behavior in infrared images, using a statistical texture image metric
AU - Aviram, G.
AU - Rotman, S. R.
N1 - Publisher Copyright:
© 2002 IEEE.
PY - 2000/1/1
Y1 - 2000/1/1
N2 - The ICOM statistical texture image metric incorporates the attributes of global texture matching and of local texture distinctness. The metric is used in this paper to predict human false detection performance (probabilities of false alarms) in both natural and enhanced infrared images, by automatic extraction of the potential false targets in the image. Comparing real experimental data with the metric products revealed very good agreement. Following this result, the metric was used to examine whether the human observer, regarding high and low levels of image clutter, behaves as a Constant False Alarm Rate (CFAR)' signal processor, or as a fixed threshold∗ signal processor. It was found that neither one of them is correct. Consequently, a modification to the known CFAR decision behavior model was suggested. The modified model considers the total number of detection decisions (true and false) made by the human observer as the adaptive parameter, instead of the number of only the false detection decisions in the case of the CFAR model. The modified model was tested and confirmed with results obtained both from natural and enhanced images.
AB - The ICOM statistical texture image metric incorporates the attributes of global texture matching and of local texture distinctness. The metric is used in this paper to predict human false detection performance (probabilities of false alarms) in both natural and enhanced infrared images, by automatic extraction of the potential false targets in the image. Comparing real experimental data with the metric products revealed very good agreement. Following this result, the metric was used to examine whether the human observer, regarding high and low levels of image clutter, behaves as a Constant False Alarm Rate (CFAR)' signal processor, or as a fixed threshold∗ signal processor. It was found that neither one of them is correct. Consequently, a modification to the known CFAR decision behavior model was suggested. The modified model considers the total number of detection decisions (true and false) made by the human observer as the adaptive parameter, instead of the number of only the false detection decisions in the case of the CFAR model. The modified model was tested and confirmed with results obtained both from natural and enhanced images.
UR - https://www.scopus.com/pages/publications/42549169946
U2 - 10.1109/EEEI.2000.924445
DO - 10.1109/EEEI.2000.924445
M3 - Conference contribution
AN - SCOPUS:42549169946
T3 - 21st IEEE Convention of the Electrical and Electronic Engineers in Israel, Proceedings
SP - 393
EP - 397
BT - 21st IEEE Convention of the Electrical and Electronic Engineers in Israel, Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 21st IEEE Convention of the Electrical and Electronic Engineers in Israel, IEEEI 2000
Y2 - 11 April 2000 through 12 April 2000
ER -