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Abstract:
Based on the "early selection" filter model and top-down
attention mechanism, a new selective attention algorithm is
developed to improve recognition performance for noisy patterns and
superimposed patterns. The selective attention algorithm
incorporates the error backpropagation rule to adapt the attention
filters for a testing input pattern and an attention cue for a
specific class. For superimposed test patterns an
attention-switching algorithm is also developed to recognize both
patterns one by one. The developed algorithms demonstrated much
higher recognition rates for corrupted digit recognition
tasks.
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