Ruttier Obstacle Classification by use of Fractional B-spline Wavelets and Moments

Abstract

Applications of wavelet analysis are widespread and cover many fields of scientific research including image processing, classification and recognition. In addition, the mathematical concept of moments has been used for many years in pattern recognition and image processing. We present a new discovered family of splines, named fractional B-splines which we used as mother wavelet functions. The resulted fractional B-spline wavelets constitute a part of the features vector used in our ruttier obstacle classification system. We compared different recognition rates obtained by the use of different mother wavelet functions, but in order to improve the recognition rates, we added first order statistics features and the seven moments of Hu. The artificial vision systems was developed having as model the human system, and therefore the objects recognition task is reduced to a classification using features extracted from images. In our case, the features vector is formed by wavelet transform of fractional B-splines and seven statistics features, followed by the seven moments of Hu.