Optimization and application of a RAM-based neural network for fast image processing tasks

Thomas Martini Joergensen; Steen Sloth Christensen; Allan Weimar Andersen; Christian Liisberg

doi:10.1117/12.188904

10 October 1994 Optimization and application of a RAM-based neural network for fast image processing tasks

Thomas Martini Joergensen, Steen Sloth Christensen, Allan Weimar Andersen, Christian Liisberg

Proceedings Volume 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision; (1994) https://doi.org/10.1117/12.188904
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

Abstract

A RAM-based neural network applicable for object detection in machine vision is considered. It is shown that it is easy to perform a crossvalidation test for the training set using this network type. This is relevant for measuring the network generalization capability (robustness). An information measure combining the concept of crossvalidation and Shannon information is proposed. We describe how this measure can be used to select the input connections of the network. The task of recognizing handwritten digits is used to demonstrate the capability of the selection strategy.

Citation Download Citation

Thomas Martini Joergensen, Steen Sloth Christensen, Allan Weimar Andersen, and Christian Liisberg "Optimization and application of a RAM-based neural network for fast image processing tasks", Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); https://doi.org/10.1117/12.188904

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