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<p>A complex autoregressive model for invariant feature extraction to recognize arbitrary shapes on a plane is presented. 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 possible to make them invariant to scale and translation. Experimental results that the 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.</p>
computer vision; statistics; rotation invariance; scale invariance; translation invariance; shape recognition; complex autoregressive model; invariant feature extraction; complex autoregressive coefficients; complex PARCOR coefficients; nonconvex boundaries; computer vision; filtering and prediction theory; statistics

N. Otsu, T. Kurita and I. Sekita, "Complex Autoregressive Model for Shape Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 489-496, 1992.
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