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.>

Complex-log conformal mapping is combined with a distributed associative memory to create a system that recognizes objects regardless of changes in rotation or scale. Information recalled 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 the approach.>

The distributed associative memory (DAM) model is proposed for distributed and fault-tolerant computation related to retrieval tasks. The fault tolerance is with respect to noise in the input key data and/or local and global failures in the memory itself. Working models for fault-tolerant image reconfiguration and database information retrieval have been developed and backed up by experimental results that show the feasibility of such an approach.>