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We have tried to illustrate some contrast sensitivity defects by picture simulation. We have used data obtained from 2 patients: a woman with optic nerve lesion (Snellen VA 0.5) and a 7.5-year-old boy with anisometropic amblyopia (VA 0.6). The optic nerve lesion was represented by profound contrast sensitivity loss for all spatial frequencies, while the anisometropic eye showed loss only at high spatial frequencies. A positive picture was decomposed into 1.25 X 10(6) pixels (picture elements), using a drum scanner. In a computer each spatial frequency component of the picture was multiplied by the ratio between the patient sensitivity value and that of an age-matched reference group and a modified image was processed. The pictures illustrate the poor image quality that is associated with general contrast sensitivity loss, even when Snellen visual acuity is only moderately impaired.

When using prediction programs for optical signatures, it is necessary to include validations to find estimates of the uncertainties and define the regions of validity. In this paper we present two paths of development of validation methods: The objective of the first path is to analyze and validate the differences between simulated and measured images, through image features such as edge concentration and different energy measures. In particular, aspects that are important for detection, classification and identification of targets are considered. The second path concerns development of methods for quantifying the propagation of input data uncertainties to output parameters in computational predictions. Two commercial codes have been used for the modeling: RadThermIR for thermal predictions of the targets and CAMEO-SIM for the radiometry and rendering. A recently developed interface between the two codes has been utilized. For the validation of spatial statistics, several feature values have been computed for a measured image and for the corresponding simulated image. It was found that the agreement was quite good. The work on propagation of uncertainties in computational predictions has resulted in a number of proposed methods. In this paper we present two different methods: one based on linear error propagation and one based on the Monte Carlo method. The results are according to expectations for both types of methods and show that a large part of the uncertainty in predicted temperature emanates from input parameter uncertainties for the considered test case.

As a part of the Swedish mine detection project MOMS, an initial field trial was conducted at the Swedish EOD and Demining Centre (SWEDEC). The purpose was to collect data on surface-laid mines, UXO, submunitions, IED's, and background with a variety of optical sensors, for further use in the project. Three terrain types were covered: forest, gravel road, and an area which had recovered after total removal of all vegetation some years before. The sensors used in the field trial included UV, VIS, and NIR sensors as well as thermal, multi-spectral, and hyper-spectral sensors, 3-D laser radar and polarization sensors. Some of the sensors were mounted on an aerial work platform, while others were placed on tripods on the ground. This paper describes the field trial and the presents some initial results obtained from the subsequent analysis.