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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 22 — Aug. 1, 2010
  • pp: 4144–4151

Neural network model for rotation invariant recognition of object shapes

Mausumi Pohit  »View Author Affiliations


Applied Optics, Vol. 49, Issue 22, pp. 4144-4151 (2010)
http://dx.doi.org/10.1364/AO.49.004144


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Abstract

A multichannel, multilayer feed forward neural network model is proposed for rotation invariant recognition of objects. In the M channel network, each channel consists of a one dimensional slice of the two dimensional (2D) Fourier transform (FT) of the input pattern that connects fully to the weight matrix. Each slice is taken at different angles from the 2D FT of the object. From each channel, only one neuron can fire in the presence of the training object. The output layer sums up the response of the hidden layer neuron and confirms the presence of the object. Rotation invariant recognition from 0 ° to 360 ° is obtained even in the case of degraded images.

© 2010 Optical Society of America

OCIS Codes
(070.4550) Fourier optics and signal processing : Correlators
(150.0150) Machine vision : Machine vision

ToC Category:
Machine Vision

History
Original Manuscript: March 1, 2010
Revised Manuscript: June 24, 2010
Manuscript Accepted: June 25, 2010
Published: July 22, 2010

Citation
Mausumi Pohit, "Neural network model for rotation invariant recognition of object shapes," Appl. Opt. 49, 4144-4151 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-22-4144


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