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The recognition of shapes in binary images using a gradient classifier

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4 Author(s)
R. D. Brandt ; Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI, USA ; Y. Wang ; A. J. Laub ; S. K. Mitra

The authors consider a prototype-based binary image classifier that makes comparisons based on blurred representations of the images. The blurring induces a metric on the space of all images that varies continuously under continuous deformation of the image plane. This blurred representation is suitable for direct implementation of a nearest-neighbor classifier. However, it is still desirable to have a representation which is invariant under certain spatial deformations, such as rotation, translation, and scaling of the image plane. A representation which is invariant under these transformation is produced by transforming an input to a local minimum of its distance from each prototype simultaneously. These minima are found by performing a gradient descent on an appropriate error surface over the transformation parameters. The error functional is the L2-norm of the difference between the blurred prototype and the blurred input. The resulting classifier makes more efficient use of prototypes than does the nearest-neighbor classifier

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:19 ,  Issue: 6 )