26 June 2001 Model observers for signal-known-statistically tasks (SKS)
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Abstract
Model observers have been successfully applied to predict human visual detection performance for tasks in which the signal is known a priori and does not vary from trial to trial (signal known exactly task). Although well understood, the signal known exactly task does not reflect aspects of real-life tasks where the signal might vary and the observer does not have full knowledge of the signal parameters. In this paper, we investigate performance in two tasks: a signal known to observers but with variable size and shape and compare it to a task where the signal is variable and not known to the observer (signal known statistically). The tasks are investigated in the context of 2-component noise (power law and white noise). We present a number of candidate multitemplate models for signal known statistically tasks that are natural extensions of the signal known exactly existing models. Human observer results show that although human performance in the signal known exactly but variable is in general better than the signal known exactly task, the differences in performance are not large and smaller than that of the ideal observer and other suboptimal models (e.g. non-prewhitening matched filter with an eye filter). For the ranges of size and shape uncertainty studied in this paper, our results suggest that the signal known exactly but variable task could be used as a first approximation to performance in the signal known statistically tasks. Therefore, the computationally simpler signal known exactly but variable task might be used in these circumstances as a figure of merit to evaluate and optimize performance in the more realistic signal known statistically tasks.
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Miguel P. Eckstein, Miguel P. Eckstein, Craig K. Abbey, Craig K. Abbey, } "Model observers for signal-known-statistically tasks (SKS)", Proc. SPIE 4324, Medical Imaging 2001: Image Perception and Performance, (26 June 2001); doi: 10.1117/12.431177; https://doi.org/10.1117/12.431177
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