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<p>Advanced correlation filter synthesis algorithms to achieve rotation invariance are described. We use a specified form for the filter as the rotation invariance constraint and derive a general closed-form solution for a multiclass rotation-invariant filter that can recognize a number of different objects. By requiring the filter to minimize the average correlation plane energy, we produce a multiclass rotation invariant (RI) RI-MACE filter, which controls correlation plane sidelobes and improves discrimination against false targets. To improve noise performance, we require the filter to minimize a weighted sum of correlation plane signal and noise energy. Initial test results of all filters are provided.</p>
filtering and prediction theory; invariance; correlation methods; image recognition; correlation filters; rotation invariance; closed form solution; multiclass rotation-invariant filter; average correlation plane energy; RI-MACE filter; noise energy; correlation plane signal; pattern recognition

G. Ravichandran and D. Casasent, "Advanced In-Plane Rotation-Invariant Correlation Filters," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 415-420, 1994.
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