Invariant image analysis based on Radon transform and SVD

A Radon-based invariant image analysis method is introduced. The linearity, shift, rotation, and scaling properties of the Radon transform are utilized to achieve invariant features to translation, rotation, and scaling. The singular values of a matrix, constructed by row-stacking of projections, are used to construct the invariant feature vector. This feature vector will be used as input to a classifier, which is here, the back-propagation neural network followed by a maximum-output-selector. A performance function is introduced to evaluate the performance of the recognition system. This performance function can also be used to indicate how closely the pattern matches the decision template. The effectiveness of this method is illustrated by a simulation example and it is compared with the method of Zernike moments.