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

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 280
  • Download : 0
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
Article Type
Article; Proceedings Paper
Appears in Collection
EE-Journal Papers(저널논문)
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0