COMPLEX AUTOREGRESSIVE MODEL FOR SHAPE-RECOGNITION

This paper presents a complex autoregressive model for invariant featu re extraction to recognize arbitrary shapes on a plane. A fast algorit hm to calculate complex autoregressive coefficients and complex PARCOR coefficients of the model is also shown. The coefficients are invaria nt to rotation around the origin and to choice of the starting point i n tracing a boundary. It is also possible to make them invariant to sc ale and translation. Experimental results show that complicated shapes like nonconvex boundaries can be recognized in high accuracy, even in the low-order model. It is seen that the complex PARCOR coefficients tend to provide more accurate classification than the complex AR coeff icients.