Sampling, resampling and colour constancy

Research output: Contribution to journalConference article

Abstract

The colour constancy as a problem of Bayesian inference is formulated, where one is trying to represent the posterior on possible interpretations on the given image data. The posterior is represented as a set of samples, drawn from the distribution using a Markov chain Monte Carlo method. This approach has the advantage that it unifies the constraints on the problem, and represents possible ambiguities. In turn, a good description of possible ambiguities means that new information, instead of producing contradictions, is easily incorporated by resampling existing samples. The method is demonstrated on the case where surfaces seen in two distinct images are later discovered to be the same.

Original languageEnglish (US)
Pages (from-to)300-305
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
StatePublished - Jan 1 1999
Externally publishedYes
EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
Duration: Jun 23 1999Jun 25 1999

Fingerprint

Markov processes
Monte Carlo methods
Sampling
Color

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Sampling, resampling and colour constancy. / Forsyth, D. A.

In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 01.01.1999, p. 300-305.

Research output: Contribution to journalConference article

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