---
abstract: 'VISOR is a large connectionist system that shows how visual schemas can be learned, represented, and used through mechanisms natural to neural networks. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. Simulations show that VISOR is robust against noise and variations in the inputs and parameters. It can indicate the confidence of its analysis, pay attention to important minor differences, and use context to recognize ambiguous objects. Experiments also suggest that the representation and learning are stable, and its behavior is consistent with human processes such as priming, perceptual reversal, and circular reaction in learning. The schema mechanisms of VISOR can serve as a starting point for building robust high-level vision systems, and perhaps for schema-based motor control and natural language processing systems as well. </blockquote>'
altloc:
  - http://www.cs.utexas.edu/users/nn/pages/publications/vision.html
  - ftp://ftp.cs.utexas.edu/pub/neural-nets/papers/leow.connsci97.ps.Z
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creators_name:
  - family: Leow
    given: Wee Kheng
    honourific: ''
    lineage: ''
  - family: Miikkulainen
    given: Risto
    honourific: ''
    lineage: ''
date: 1997
date_type: published
datestamp: 1999-01-03
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dir: disk0/00/00/05/26
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keywords: 'visual schemas, schema learning, priming, perceptual reversal, circular reaction, schema hierarchy, hierarchical learning, robustness'
lastmod: 2011-03-11 08:54:01
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metadata_visibility: show
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pagerange: 161-200
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publication: Connection Science
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rev_number: 10
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status_changed: 2007-09-12 16:30:06
subjects:
  - comp-sci-art-intel
  - comp-sci-mach-vis
  - comp-sci-neural-nets
  - percep-cog-psy
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title: Visual Schemas in Neural Networks for Object Recognition and Scene Analysis
type: journalp
userid: 438
volume: 9
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