A new concept for invariant pattern recognition is presented that uses object contour information. First, an angular signature of the object contour is obtained by a nonlinear operation applied to two-dimensional directional convolutions with a long, narrow kernel. The angular signature function is normalized by either its area or its energy to achieve quasi-invariance to scale. The resulting signature is then compared with template signatures for the invariant recognition for which an angular similarity measure is obtained from a one-dimensional correlation between the two signatures. Numerical experiments demonstrate that the method discussed exhibits invariance to shift and angular orientation and quasi-invariance to scale.
© 1993 Optical Society of America
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