Recognition of rotating images using an automatic feature extraction technique and neural networks

This paper presents a new automatic feature extraction technique and a neural network based classification method for recognition of rotating images. The image processing technique extracts global features of an image and converts a large size image into a one-dimensional small vector. A special advantage of the proposed technique is that the extracted features are the same even if the original image is rotated with rotation angles from 5 to 355 or rotated and little bit distorted. The proposed technique is based on simple co-ordinate geometry fuzzy sets and neural networks. The proposed approach is very easy in implementation and it has implemented in C++ on a Sun workstation. The experimental results have demonstrated that the proposed approach performs successfully on a variety of small as well as large scale rotated and distorted images.