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

Abstract

In this paper we introduce a multi-scale Gaussian Markov random field (GMRF) model and a corresponding anomaly subspace detection algorithm. Natural clutter images, often appear to have several periodical patterns of various period lengths. In such cases, the GMRF model may not sufficiently describe the clutter image. The proposed model is based on a multi-scale wavelet representation of the image, independent component analysis, and modeling each independent component as a GMRF. Anomaly detection is subsequently carried out by applying a matched subspace detector to the innovations process generated by the presumed model. The robustness of the proposed approach is demonstrated with application to automatic target detection in synthetic and real imagery. A quantitative performance analysis and experimental results demonstrate the advantage of the proposed method in comparison to competing methods. r

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“…Alternative approaches to investigate the internal structure of objects using wideband acoustics [23,24] are showing some promises, but it is now widely acknowledged that current techniques are reaching their limits. Yet, their performances do not enable rapid and effective mine clearance and false alarm rates remain prohibitively high [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. This is not a critical problem when operators can validate the outputs of the algorithms directly, as they still enable a very high data compression rate by dramatically reducing the amount of information that an operator has to review.…”

Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification

“…Work has generally focusing on either just SCM or RIN with the former most interested in being fast and the latter in being accurate [4]. Several innovative algorithms have been proposed for SCM, including those based on diffusion maps [17], Markov random field models [18]- [20], windowed area pixel intensity thresholds [21], and a locality-base graph models [22]. These schemes have the difficult task of locating mines even when faced with substantial background clutter, a common attribute of Sonar images.…”