Application of the Cascade-Correlation network to the classification of circularly scanned images

This paper describes an application of the Cascade-Correlation (CC) network to pattern recognition. The pattern recognition task was to simulate an automatic vision inspection system that had to properly classify five different objects. The feature vectors were extracted from 2D images of circularly scanned images and used as inputs for a neural network that was then trained to classify an unknown presented object. The results show that the CC network is viable tool in pattern recognition tasks. It is able to classify partially occluded objects with high accuracy, and to considerably improve classification of noisy images based on simple histogram trimming preprocessing.