Distributed Associative Memory (DAM) for Bin-Picking
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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.
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Index Terms:
computerised pattern recognition; distributed associative memory; bin-picking system; invariance; metric distortions; noise; occlusions; neural network model; computer vision; computer vision; computerised pattern recognition; content-addressable storage; neural nets; parallel processing
Citation:
H. Wechsler, G.L Zimmerman, "Distributed Associative Memory (DAM) for Bin-Picking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 8, pp. 814-822, Aug. 1989, doi:10.1109/34.31444
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