Probabilistic image processing by extended Gauss-Markov random fields

Kazuyuki Tanaka, Nicolas Morin, Muneki Yasuda, D. M. Titterington

研究成果: Conference contribution

抄録

We propose an extension of the Gauss-Markov random field (GMRF) models by introducing next-nearest neighbour interactions. The values of the next-nearest neighbour interactions are set to positive real numbers with the expectation that this will lead to some noise reduction while preserving the edges. Values for the hyperparameters in the proposed model are determined by using the EM algorithm in order to maximize the marginal likelihood. In addition, a measure of mean squared error, which quantifies the statistical performance of our proposed model, is derived analytically as an exact explicit expression by means of the multi-dimensional Gaussian integral formulas.

本文言語English
ホスト出版物のタイトル2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
ページ618-621
ページ数4
DOI
出版ステータスPublished - 2009 12 25
イベント2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 - Cardiff, United Kingdom
継続期間: 2009 8 312009 9 3

出版物シリーズ

名前IEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
国/地域United Kingdom
CityCardiff
Period09/8/3109/9/3

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 応用数学
  • 信号処理
  • コンピュータ サイエンスの応用

フィンガープリント

「Probabilistic image processing by extended Gauss-Markov random fields」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル