Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Simulation, Knowledge-Based Computing, and Fuzzy Statistics
Simulation, Knowledge-Based Computing, and Fuzzy Statistics
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Interpreting spectral images requires comparing known patterns with input data (images) to identify which patterns are contained in the input data. In practice, however, it is hard to identify any pattern when the inaccuracy of input data is not slight. In this paper, we present a method for interpreting spectral images by using qualitative reasoning. First, we put forward a new concept called support coefficient function (SCF) which can be used to extract, represent, and calculate qualitative correlations among data. Then, we introduce an approach to determining dynamic shift intervals of inaccurate data on the basis of qualitative correlations. Finally, we discuss how to use qualitative correlations as evidence of enhancing or depressing hypotheses for in accurate data. The method has been applied to a practical system for interpreting infrared spectral images. We have fully tested the system against several hundred real spectral images. The rate of identification (RI) and the rate of correctness (RC) are near 90% and 74% respectively, and the latter is the highest among known systems.