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 a 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 been developed 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.
Verma
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School of Information Technology, Faculty of Engineering and Applied Science, Griffith University-Gold Coast Campus, Qld, Australia. B.Verma@eas.gu.edu.au
D000465:Algorithms; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012399:Rotation; D012984:Software