Image classification by neural networks using moment invariant feature vectors / by Kenneth L. Sala.: Co24-3/7-97-002E-PDF

"An image classification system based upon the extraction of moment invariant feature vectors and an artificial neural network classifier is described. The moment invariant feature vectors are derived from the test images using series of orthogonal basis functions. Six different basis functions are studied which include four types of Zernike functions and two types of Walsh functions. Four different schemes for the normalization of the feature vectors are also investigated. The images used in the study possess random scales, lateral positions, and angles of orientation in the image plane. In addition, random noise with different signal-to-noise ratios is superimposed upon the images. The feature vector extraction technique employs the concept of moment invariants so that the feature vector components are independent of the image's scale, lateral position, and orientation. The neural network employed for the classification task is a multilayer perceptron network which is trained with the backpropagation algorithm. The performance of the overall classification system is determined by measuring the classification accuracy as a function of the signal-to-noise ratio of the test imagery. The work and the results presented in this study form the basis for a neural network based, image recognition system which will be employed in the classification of military, synthetic aperture radar (SAR) imagery of land targets"--Abstract, page iii.

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