Variations on regularization

Daniel Keren, Michael Werman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Regularization has become an important tool for solving many ill-posed problems in approximation theory--for example, in computer vision--including surface reconstruction, optical flow, and shape from shading. The authors attempt to determine whether the approach taken in regularization is always the correct one, and to what extent the results of regularization are reliable. For example, the authors consider a case in which regularization has been used to reconstruct a surface from sparse data and attempt to determine how strongly the height of the surface at a certain point can be relied upon. These questions are answered by defining a probability distribution on the class of surfaces considered, and computing its expectation and variance. The variance can be used, for instance, to construct a safety strip around the interpolated surface that should not be entered if collision with the surface is to be avoided.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherPubl by IEEE
Number of pages6
ISBN (Print)0818620625
StatePublished - 1990
Externally publishedYes
EventProceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA
Duration: 16 Jun 199021 Jun 1990

Publication series

NameProceedings - International Conference on Pattern Recognition


ConferenceProceedings of the 10th International Conference on Pattern Recognition
CityAtlantic City, NJ, USA

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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