Distributed Associative Memory (DAM) for bin-picking

Harry Wechsler, George Lee Zimmerman

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


The feasibility of using a distributed associative memory as the recognition component for a bin-picking system is established. The system displays invariance to metric distortions and a robust response in the presence of noise, occlusions, and faults. Although the system is primarily concerned with two-dimensional problems, eight extensions to the system allow the three-dimensional bin-picking problem to be addressed. It is noted that there are implicit weaknesses in the neural network model chosen for the heart of the recognition system. The distributed associative memory used is linear, and as a result there are certain desirable properties that cannot be exhibited by the computer vision system.

Original languageEnglish (US)
Pages (from-to)814-822
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number8
StatePublished - 1989

Bibliographical note

Funding Information:
Manuscript received March 9, 1987; revised July 6, 1988. Recommended for acceptance by J. L. Mundy. This work was supported in part by the National Science Foundation under Grant ECS-8310057 and by a grant from the Microelectronics and Information Science (MEIS) Center of the University of Minnesota.


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