book · 1997

Symbolic Visual Learning

Cited by 72Abstract and details only

Abstract

Abstract Some of the fundamental constraints of automated machine vision have been the inability automatically to adapt parameter settings or utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to computer vision systems. Using this state-of-the-art recognition technology, the outcome is different adaptive recognition systems that can measure their own performance, learn from their experience and outperform conventional static designs. Written as a companion volume to Early Visual Learning (edited by S. Nayar and T. Poggio), this book is intended for researchers and students in machine vision and machine learning.

Cite this paper

APA 7

(1997). Symbolic Visual Learning. https://doi.org/10.1093/oso/9780195098709.001.0001

MLA 9

“Symbolic Visual Learning.” 1997, https://doi.org/10.1093/oso/9780195098709.001.0001.

Chicago

1997. “Symbolic Visual Learning.” https://doi.org/10.1093/oso/9780195098709.001.0001.

Harvard

(1997) ‘Symbolic Visual Learning’. Available at: https://doi.org/10.1093/oso/9780195098709.001.0001.

BibTeX

@book{anon1997symbolic,
  title = {{Symbolic Visual Learning}},
  year = {1997},
  doi = {10.1093/oso/9780195098709.001.0001}
}

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