TRANSLATION, ROTATION AND SCALE-INVARIANT PATTERN-RECOGNITION USING SPECTRAL-ANALYSIS AND HYBRID GENETIC-NEURAL-FUZZY NETWORKS

A two dimensional image recognition method using spectral analysis and hybrid network classifiers was developed. The feature vectors using s pectral analysis on normalized centroidal distance sequences of each i mage were extracted. The hybrid network classifiers using the advantag es of conventional methods which are gradient-descent-searching backpr opagation network (BPN), global searching genetic algorithm (GA), and fuzzy c-means algorithm (FCMA) were developed. The proposed method is applied to the recognition of aircraft, letters (Arabic numerals and E nglish alphabet) and machine tools. The experimental results show that the proposed method has a higher accuracy, averaging 3.2% than BPN at a noise rate of 13 dB-25 dB, and the training times can be shortened by half of BPN while maintaining the same performance. Copyright (C) 1 996 Elsevier Science Ltd