Outlier process

Davi Geiger, Ricardo Alberto Marques Pereiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We discuss the problem of detecting outliers from a set of surface data. We start from the Bayes approach and the assumption that surfaces are (i) piecewise smooth and (ii) corrupted by a combination of white Gaussian and salt and pepper noise. We show that we can model such surfaces by introducing an outlier process that is capable of 'throwing away' data. We make use of mean field techniques to finally obtain a deterministic network. The experimental results with real images support the model.

Original languageEnglish (US)
Title of host publicationNeural Networks for Signal Processing
PublisherPubl by IEEE
Pages60-69
Number of pages10
ISBN (Print)0780301188
Publication statusPublished - 1991
EventProceedings of the 1991 Workshop on Neural Networks for Signal Processing - NNSP-91 - Princeton, NJ, USA
Duration: Sep 30 1991Oct 2 1991

Other

OtherProceedings of the 1991 Workshop on Neural Networks for Signal Processing - NNSP-91
CityPrinceton, NJ, USA
Period9/30/9110/2/91

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Geiger, D., & Marques Pereiro, R. A. (1991). Outlier process. In Neural Networks for Signal Processing (pp. 60-69). Publ by IEEE.