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 journalArticle

Abstract

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 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 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.

Original languageEnglish (US)
Pages (from-to)1595-1599
Number of pages5
JournalIEEE Transactions on Systems, Man and Cybernetics
Volume19
Issue number6
DOIs
StatePublished - Nov 1989

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Binary images
Classifiers

ASJC Scopus subject areas

  • Engineering(all)

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Recognition of shapes in binary images using a gradient classifier. / Brandt, Robert D.; Wang, Yao; Laub, Alan J.; Mitra, Sanjit K.

In: IEEE Transactions on Systems, Man and Cybernetics, Vol. 19, No. 6, 11.1989, p. 1595-1599.

Research output: Contribution to journalArticle

Brandt, Robert D. ; Wang, Yao ; Laub, Alan J. ; Mitra, Sanjit K. / Recognition of shapes in binary images using a gradient classifier. In: IEEE Transactions on Systems, Man and Cybernetics. 1989 ; Vol. 19, No. 6. pp. 1595-1599.
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