Irregular-Closed Graphic Recognition Based on Contour Measurement Matrix

For higher efficiency of irregular-closed graphic recognition than the traditional algorithms,a self-defined contour measurement matrix is introduced,and based on which a fast recognition algorithms is proposed．The contour matrix optimally describes the geometric characteristic of the graphic outline,then a group of eccentric-rate parameters is defined based on the matrix,and the probability density function of the parameters is considered as the basis of classification．Combined with Bayes theory,a design method of classification with minimum-error probability （ MEP ） is proposed．The proposed algorithm is demonstrated by applications to be invariant to translation and scaling,is about 30times more computationally efficient than the Fourier-based descriptors．