Assessment of synthetic image fidelity

Kevin D. Mitchell; Ian R. Moorhead; Marilyn A. Gilmore; Graham H. Watson; Mitch Thomson; T. Yates; Tomasz Troscianko; David J. Tolhurst

doi:10.1117/12.392533

24 July 2000 Assessment of synthetic image fidelity

Kevin D. Mitchell, Ian R. Moorhead, Marilyn A. Gilmore, Graham H. Watson, Mitch Thomson, T. Yates, Tomasz Troscianko, David J. Tolhurst

Author Affiliations +

Proceedings Volume 4029, Targets and Backgrounds VI: Characterization, Visualization, and the Detection Process; (2000) https://doi.org/10.1117/12.392533
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Computer generated imagery is increasingly used for a wide variety of purposes ranging from computer games to flight simulators to camouflage and sensor assessment. The fidelity required for this imagery is dependent on the anticipated use - for example when used for camouflage design it must be physically correct spectrally and spatially. The rendering techniques used will also depend upon the waveband being simulated, spatial resolution of the sensor and the required frame rate. Rendering of natural outdoor scenes is particularly demanding, because of the statistical variation in materials and illumination, atmospheric effects and the complex geometric structures of objects such as trees. The accuracy of the simulated imagery has tended to be assessed subjectively in the past. First and second order statistics do not capture many of the essential characteristics of natural scenes. Direct pixel comparison would impose an unachievable demand on the synthetic imagery. For many applications, such as camouflage design, it is important that nay metrics used will work in both visible and infrared wavebands. We are investigating a variety of different methods of comparing real and synthetic imagery and comparing synthetic imagery rendered to different levels of fidelity. These techniques will include neural networks (ICA), higher order statistics and models of human contrast perception. This paper will present an overview of the analyses we have carried out and some initial results along with some preliminary conclusions regarding the fidelity of synthetic imagery.

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Kevin D. Mitchell, Ian R. Moorhead, Marilyn A. Gilmore, Graham H. Watson, Mitch Thomson, T. Yates, Tomasz Troscianko, and David J. Tolhurst "Assessment of synthetic image fidelity", Proc. SPIE 4029, Targets and Backgrounds VI: Characterization, Visualization, and the Detection Process, (24 July 2000); https://doi.org/10.1117/12.392533

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KEYWORDS

Neurons

Sensors

Visual process modeling

Camouflage

Target detection

Human vision and color perception

Neural networks

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