Visual target discrimination has occurred when the observer can say "I see a target THERE!" and can designate the target location. Target discrimination occurs when a perceived shape is sufficiently similar one or more of the instances the observer has been trained on. Marr defined vision as "knowing what is where by seeing." Knowing "what" requires prior knowledge. Target discrimination requires model-based visual processing. Model-based signature metrics attempt to answer the question "to what extent does the target in the image resemble a training image?" Model-based signature metrics attempt to represent the effects of high-level top-down visual cognition, in addition to low-level bottom-up effects. Recent advances in realistic 3D target rendering and computer-vision object recognition have made model-based signature metrics more practical. The human visual system almost certainly does NOT use the same processing algorithms as computer vision object recognition, but some processing elements and the overall effects are similar. It remains to be determined whether model-based metrics explain the variance in human performance. The purpose of this paper is to explain and illustrate the model-based approach to signature metrics.
The modeling and analysis of infrared target and background signatures continues to be a topic of interest
in the DoD. The question in many individuals minds is: What is the purpose of these signature prediction
activities? After all, the sensor perfonnance modeling community tends toward the use of simplistic target and
background representations in their models. Typically signature inputs to sensor models are nothing more than
target delta temperatures which assume homogeneous targets and backgrounds. This paper attempts to answer
the following two questions: 1 .Why do current sensor models have moderate to high fidelity modeling of the
sensor subsystems and systems performance and low fidelity input target/background signatures? and 2. Is a
high fidelity target and backgmund signature required to provide a meaningful estimate of sensor perfonnance?
Examples of current sensor models and predictive target and background signature models are provided and
discussed. A challenge is issued to the sensor modelers to learn more about signature prediction models and to
be innovative in their use in development of future sensor perfonnance methodologies.
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