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Computation of pattern invariance in brain-like structures.

S Ullman1, S Soloviev

  • 1Department of Applied Mathematics & Computer Science, The Weizmann Institute of Science, Rehovot, Israel

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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This study introduces a novel computational approach for invariant recognition in artificial systems, enabling generalization from past experiences to recognize familiar objects under new viewing conditions.

Area of Science:

  • * Computational Neuroscience
  • * Artificial Intelligence
  • * Computer Vision

Background:

  • * The brain's ability to generalize and recognize objects under novel conditions is a complex computational challenge.
  • * Replicating this generalization capability in artificial systems has proven difficult.
  • * Existing artificial systems struggle with invariant recognition, especially concerning positional variations.

Purpose of the Study:

  • * To present a new computational approach for generalization and invariant recognition.
  • * To address the challenge of shift invariance in visual pattern recognition.
  • * To explore the extension of these principles to broader invariant perception and classification.

Main Methods:

  • * Development of a system utilizing a large repertoire of partial generalizations derived from past experience.

Related Experiment Videos

  • * Representing visual patterns as conjunctions of multiple, overlapping image fragments.
  • * Building invariance to primitive fragments through experience and extending it to complex shapes.

    • Main Results:

    • * Simulations demonstrate the effectiveness of the proposed method for achieving shift invariance.
    • * The approach shows promise in enabling complex shapes to be recognized invariantly.
    • * The study lays groundwork for extending these principles to brain-like invariant perception.

    Conclusions:

    • * The proposed method offers a viable approach to achieving generalization and invariant recognition in artificial systems.
    • * By building invariance from primitive fragments, complex pattern recognition can be achieved.
    • * This research contributes to the development of more sophisticated and brain-like artificial perception systems.