Feature extraction by fractal dimensions

Proposes a method that reduces the dimensionality of a 2D pattern by means of a central projection approach, and thereafter performs a Daubechies wavelet transformation on the derived 1D pattern to generate a set of wavelet transformation sub-patterns, namely curves that are non-self-intersecting. Further, from the resulting non-self-intersecting curves, the divider dimensions are compared with the modified box-counting approach. These divider dimensions constitute a new feature vector for the original 2D pattern, defined over the curve´s fractal dimensions