Top-down selective attention for robust perception of noisy and confusing patterns

A neural network model is developed for the top-down selective attention (TDSA), which estimates the most probable sensory input signal based on previous knowledge and filters out irrelevant sensory signals for high-confidence perception of noisy and confusing signals. The TDSA is modeled as an adaptation process to minimize the attention error, which is implemented by the error backpropagation algorithm for the multilayer Perceptron classifiers. Sequential recognition of superimposed patterns one by one is also possible. The developed TDSA model is applied to the recognition tasks of two-pattern images, superimposed handwritten characters, and noise-corrupted speeches.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2004
Language
ENG
Keywords

MODEL

Citation

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2004 BOOK SERIES: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, v.3070, pp.73 - 78

ISSN
0302-9743
URI
http://hdl.handle.net/10203/10218
Appears in Collection
EE-Journal Papers(저널논문)
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