Modeling Textured Images Using Generalized Long Correlation Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-class Markovian segmentation of high-resolution sonar images
Computer Vision and Image Understanding
Dimensionality Reduction for Similarity Searching in Dynamic Databases
Dimensionality Reduction for Similarity Searching in Dynamic Databases
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Anomaly detection based on an iterative local statistics approach
Signal Processing
Hyperspectral imagery: clutter adaptation in anomaly detection
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
A simple unsupervised MRF model based image segmentation approach
IEEE Transactions on Image Processing
Efficient detection in hyperspectral imagery
IEEE Transactions on Image Processing
Long-correlation image models for textures with circular and elliptical correlation structures
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
High-resolution sonars: what resolution do we need for target recognition?
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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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.