The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
A survey of image registration techniques
ACM Computing Surveys (CSUR)
A Bidirectional Associative Memory Based on Optimal Linear Associative Memory
IEEE Transactions on Computers
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Functional Representation of Recalling Process and Memory Capacity in Associative Memory
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Directional Relations Composition by Orientation Histogram Fusion
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
A new strategy for designing bidirectional associative memories
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A correlation-based subspace tracking algorithm
IEEE Transactions on Signal Processing
Unsupervised learning in noise
IEEE Transactions on Neural Networks
Two coding strategies for bidirectional associative memory
IEEE Transactions on Neural Networks
Stability analysis of bidirectional associative memory networks with time delays
IEEE Transactions on Neural Networks
Encoding strategy for maximum noise tolerance bidirectional associative memory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A bidirectional heteroassociative memory for binary and grey-level patterns
IEEE Transactions on Neural Networks
An analysis of high-capacity discrete exponential BAM
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
A maximum-likelihood-criterion based bidirectional associative memory network (hereinafter, the MLBAM network) is presented, which can be employed to evaluate the similarity between a template and a matching region. Furthermore, the analysis on the stability and the convergence of learning rule of the network is conducted. The results show that the MLBAM network is capable of associating two templates (big and small) and thus greatly reducing the computational load by using coarse-to-fine hierarchical strategy. Finally, an experiment on the target tracking of MLBAM network is conducted using a group of robots operating on a football field, demonstrating the high efficiency of the method.