Anomaly subspace detection based on a multi-scale Markov random field model

Arnon Goldman; Israel Cohen

Anomaly subspace detection based on a multi-scale Markov random field model

Research output: Chapter in Book/Report/Conference proceeding › Foreword/postscript

Abstract

In this paper, we introduce a multi-scale Gaussian Markov random field (GMRF) model and a corresponding anomaly subspace detection algorithm. The proposed model is based on a multi-scale wavelet representation of the image, independent components analysis (ICA), and modeling each independent component as a GMRF. The anomaly detection is subsequently carried out by applying matched subspace detector (MSD) to the innovations process of the GMRFs, incorporating a priori information about the targets. The robustness of the proposed approach is demonstrated with application to automatic detection of airplanes on synthetic cloudy sky backgrounds.

Original language	English
Title of host publication	2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel
Pages	444-447
Number of pages	4
State	Published - 2004
Event	2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings - Tel-Aviv, Israel Duration: 6 Sep 2004 → 7 Sep 2004

Conference

Conference	2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings
Country/Territory	Israel
City	Tel-Aviv
Period	6/09/04 → 7/09/04

ASJC Scopus subject areas

Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Access to Document

https://ieeexplore.ieee.org/document/1361187

Cite this

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abstract = "In this paper, we introduce a multi-scale Gaussian Markov random field (GMRF) model and a corresponding anomaly subspace detection algorithm. The proposed model is based on a multi-scale wavelet representation of the image, independent components analysis (ICA), and modeling each independent component as a GMRF. The anomaly detection is subsequently carried out by applying matched subspace detector (MSD) to the innovations process of the GMRFs, incorporating a priori information about the targets. The robustness of the proposed approach is demonstrated with application to automatic detection of airplanes on synthetic cloudy sky backgrounds.",

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Anomaly subspace detection based on a multi-scale Markov random field model

Abstract

Conference

ASJC Scopus subject areas

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