Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
2-D Invariant Object Recognition Using Distributed Associative Memory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Computer Vision
Selective and Focused Invariant Recognition Using Distributed Associative Memories (DAM)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized bidirectional associative memories for image processing
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation
Machine Vision and Applications
Reliable face recognition using adaptive and robust correlation filters
Computer Vision and Image Understanding
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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.