Shift- and rotation-invariant interpattern heteroassociation model

Francis T. Yu, Chii Maw Uang, Shizhuo Yin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A shift and rotation invariant neural network using interpattern hetero association (IHA) model is illustrated. To preserve the shift and rotation invariant properties, a set of binarized-encoded circular harmonic expansion (CHE) function at the Fourier domain is used as the training set. The interconnection weight matrix is constructed using an IHA model. By using the shift and symmetric properties of the modulus Fourier spectral, the problem of centering the CHE functions can be avoided. Computer simulations and experimental demonstrations are provided in which we have shown that the shift and rotation invariant properties of the proposed IHA neural net are indeed preserved.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsDavid P. Casasent
PublisherPubl by Society of Photo-Optical Instrumentation Engineers
Number of pages10
ISBN (Print)0819411957
StatePublished - Dec 1 1993
EventOptical Pattern Recognition IV - Orlando, FL, USA
Duration: Apr 13 1993Apr 14 1993

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherOptical Pattern Recognition IV
CityOrlando, FL, USA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Shift- and rotation-invariant interpattern heteroassociation model'. Together they form a unique fingerprint.

Cite this