Wavelets and moments for obstacle classification

The artificial vision systems was developed having as model the human system, and therefore the objects recognition task is reduced to a classification: the recognition of an initial unknown object through detection of the similarities to another object, previously learned. Our purpose is to study the obstacle recognition in the ruttier scene using wavelet transform. We compared different recognition rates obtained by the use of different mother wavelet functions (as Daubechies, Coiflet, Biorthogonal and the recent discovered ones, named fractional B- splines). In order to improve the recognition rates, we added first order statistics features and the seven moments of Hu.