Computational vision and regularization theory View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1985-09

AUTHORS

Tomaso Poggio, Vincent Torre, Christof Koch

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. More... »

PAGES

314-319

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/317314a0

DOI

http://dx.doi.org/10.1038/317314a0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016903244

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/2413361


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