### ASA 125th Meeting Ottawa 1993 May

## 3aUW4. Neural computing techniques for acoustic image processing.

**Mark Dzwonczyk
**

**
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*Sensor and Commun. Electron. Div., C. S. Draper Lab., 555 Technology
Square, Cambridge, MA 02139
*

*
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Computational architectures modeled after the biological nervous systems,
so-called neural networks, have been shown to excel at pattern classification
problems where the input data set is large and corrupted by noise and the
solution formulation is not well defined. This paper discusses the application
of neural computing techniques to a target detection problem with side-scan
sonar data. A brief review of the fundamental concepts of feed-forward neural
networks is first presented. Image segmentation and classification with a large
analog electronic neural network are then described. It is shown that such
neural computing approaches can be expected to be more robust than conventional
statistical classifiers and are far better suited for deployed, real-time
implementation of the system architecture.