Title :
Maximum a posteriori approach to object recognition with distributed sensors
Author_Institution :
Illinois Univ., Chicago, IL
fDate :
5/1/1988 12:00:00 AM
Abstract :
The maximum a posteriori (MAP) estimation concept is applied to the problem of object recognition with several distributed sensors. It is shown that in binary object recognition the MAP object recognition also minimizes the mean-square error. Simulation results show that the performance of the MAP object recognition is, in general, at least as good as the best performance by the sensors used
Keywords :
pattern recognition; picture processing; MAP; binary object recognition; distributed sensors; estimation concept; maximum a posteriori; mean-square error; object recognition; performance; Cost function; Degradation; Density functional theory; Density measurement; Image recognition; Image sensors; Noise robustness; Object detection; Object recognition; Testing;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
