Generating and coding of fractal graphs by neural network and mathematical morphology methods

We present an algorithm for generating a class of self-similar (fractal) graphs using simple probabilistic logic neuron networks and show that the graphs can be represented by a set of compressed encoding. An algorithm for quickly finding the coding, i.e., recognizing the corresponding graphs, is given and the coding are shown to be optimal (i.e., of minimal length). The same graphs can also be generated by a mathematical morphology method. These results may possibly have applications in image compression and pattern recognition.