Automatic signalized point recognition with feed-forward neural network

The recognition and accurate location of specific patterns, such as of special targets or signalized points in digital images, is an important step in photogrammetric measurement procedures. This paper explores the capability of the feed-forward neural network using a version of back-propagation training for the recognition of targets that appear in digitized images of aerial photographs. These targets commonly appear with differing orientations, backgrounds, scales, and suffer from varying shape distortions. Thus, for the network to establish an appropriate representation it must be trained with a very large number of cases that adequately reflect the variations of the target and non-target patterns. In order to eliminate redundancy and minimize the size of the training set, an iterative training scheme for the selection of such a set was developed. After two iterations of training promising results were reached