Invariant image recognition by neural networks and modified moment invariants

In this paper, a neural networks based approach for distortion invariant image recognition is presented. To reduce the dimension of the required networks, as well as to achieve invariancy, six distortion-invariant feature are extracted from each image and are used as inputs to the neural networks. These six features are derived from the modified geometrical moments of the image, which are calculated through a corrected discrete formula for computing moments more accurately. A multilayer perceptron network trained by the back-propagation algorithm can carry out the classification based on the above features. Experimental results on industrial tools and character recognition are to be given.