Cellular neural networks and PCA neural networks based rotation/scale invariant texture classification

We proposed a new index, which can be used to classify the texture image. Because of the adjustment of image capture device or the distortion of image capture, the texture image may be transformed. Usually those transformations included rotation and scale. The proposed method provides an algorithm to avoid those effects respectively. This approach is the combination of cellular neural networks and principle component analysis neural networks. This fact implies it is a feed-forward neural network, and it does not need any training set.