2-D Invariant Object Recognition Using Distributed Associative Memory

Harry Wechsler, George Lee Zimmerman

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


This paper describes an approach to two-dimensional object recognition. Complex-log conformai mapping is combined with a distributed associative memory to create a system which recognizes objects regardless of changes in rotation or scale. Recalled information from the memorized database is used to classify an object, reconstruct the memorized version of the object, and estimate the magnitude of changes in scale or rotation. The system response is resistant to moderate amounts of noise and occlusion. Several experiments, using real, gray scale images, are presented to show the feasibility of our approach.

Original languageEnglish (US)
Pages (from-to)811-821
Number of pages11
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number6
StatePublished - Nov 1988
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received November 5, 1986; revised October 27, 1987. Recommended for acceptance by C. Brown. This work was supported in part by the National Science Foundation under Grant ECS-83 10057 and by a grant from the Microelectronics and Information Science (MEIS) Center of the University of Minnesota.


  • Complex-log mapping
  • distributed associative memory
  • invariance
  • pattern recognition
  • space variant filtering


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