COMPLEX AUTOREGRESSIVE MODEL FOR SHAPE-RECOGNITION

This paper presents a complex autoregressive model for invariant feature extraction to recognize arbitrary shapes on a plane. A fast algorithm to calculate complex autoregressive coefficients and complex PARCOR coefficients of the model is also shown. The coefficients are invariant to rotation around the origin and to choice of the starting point in tracing a boundary. It is also possible to make them invariant to scale 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 coefficients.