## Statistical Performance of Cascaded Linear Shift-Invariant Processing

Applied Optics, Vol. 39, Issue 32, pp. 5949-5955 (2000)

http://dx.doi.org/10.1364/AO.39.005949

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### Abstract

The cascaded correlator architecture comprises a series of traditional linear correlators separated by nonlinear threshold functions, trained with neural-network techniques. We investigate the shift-invariant classification performance of cascaded correlators in comparison with optimum Bayes classifiers. Inputs are formulated as randomly generated sample members of known statistical class distributions. It is shown that when the separability of true and false classes is varied in both the first and the second orders, the two-stage cascaded correlator shows performance similar to that of the optimum quadratic Bayes classifier throughout the studied range. It is shown that this is due to the similar decision boundaries implemented by the two nonlinear classifiers.

© 2000 Optical Society of America

**OCIS Codes**

(070.4550) Fourier optics and signal processing : Correlators

(100.1160) Image processing : Analog optical image processing

(100.5010) Image processing : Pattern recognition

(150.0150) Machine vision : Machine vision

(150.3040) Machine vision : Industrial inspection

(200.4260) Optics in computing : Neural networks

**Citation**

Stuart Reed and Jeremy Coupland, "Statistical Performance of Cascaded Linear Shift-Invariant Processing," Appl. Opt. **39**, 5949-5955 (2000)

http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-39-32-5949

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