Maximum a posteriori approach to object recognition with distributed sensors

Maximum a posteriori approach to object recognition with distributed sensors

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 o...

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Veröffentlicht in:IEEE transactions on aerospace and electronic systems 1988-05, Vol.24 (3), p.309-313
1. Verfasser: Demirbas, K.
Format: Artikel
Sprache:eng
Schlagworte:
Cost function
Degradation
Density functional theory
Density measurement
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Image recognition
Image sensors
Imaging and optical processing
Noise robustness
Object detection
Object recognition
Optics
Physics
Testing
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Beschreibung
Zusammenfassung: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.< >
ISSN:0018-9251
1557-9603
DOI:10.1109/7.192105