Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 814-822 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 11 |
| Issue number | 8 |
| DOIs | |
| State | Published - 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.