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Computational vision and regularization theory

Nature volume 317, pages 314–319 (1985)Cite this article

Abstract

Descriptions of physical properties of visible surfaces, such as their distance and the presence of edges, must be recovered from the primary image data. Computational vision aims to understand how such descriptions can be obtained from inherently ambiguous and noisy data. A recent development in this field sees early vision as a set of ill-posed problems, which can be solved by the use of regularization methods. These lead to algorithms and parallel analog circuits that can solve ‘ill-posed problems’ and which are suggestive of neural equivalents in the brain.

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Author notes

  1. Vincent Torre: Istituto di Fisica, Universita di Genova, Genova, Italy

Authors and Affiliations

  1. Artificial Intelligence Laboratory and Center for Biological Information Processing, Massachusetts Institute of Technology, 545 Technology Square, Cambridge, Massachusetts, 02193, USA

    Tomaso Poggio, Vincent Torre & Christof Koch

Authors
  1. Tomaso Poggio
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  2. Vincent Torre
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  3. Christof Koch
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Poggio, T., Torre, V. & Koch, C. Computational vision and regularization theory. Nature 317, 314–319 (1985). https://doi.org/10.1038/317314a0

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