The Recognition of Shapes In Binary Images Using a Gradient Classifier

Robert D. Brandt, Yao Wang, Alan J. Laub, Sanjit K. Mitra

Research output: Contribution to journalArticlepeer-review


A prototype-based shape classifier that makes comparisons based on blurred representations of binary images is considered. 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 that is invariant under certain spatial deformations, such as rotation, translation, and scaling of the image plane. A representation that is invariant under these transformations 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 defined over the transformation parameters. The error functional is the $$2-norm of the difference between the blurred prototype and the blurred input. The resulting classifier makes more efficient use of prototypes than the nearest neighbor classifier.

Original languageEnglish (US)
Pages (from-to)1595-1599
Number of pages5
JournalIEEE Transactions on Systems, Man and Cybernetics
Issue number6
StatePublished - 1989

ASJC Scopus subject areas

  • General Engineering


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