A multiresolution watermark for digital images
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Voice activity detection based on adjustable linear prediction and GARCH models
Speech Communication
Speckle suppression in SAR images using the 2-D GARCH model
IEEE Transactions on Image Processing
Defect detection in patterned wafers using anisotropic kernels
Machine Vision and Applications
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
AR-GARCH in Presence of Noise: Parameter Estimation and Its Application to Voice Activity Detection
IEEE Transactions on Audio, Speech, and Language Processing
Hyperspectral imagery: clutter adaptation in anomaly detection
IEEE Transactions on Information Theory
Wavelet transform methods for object detection and recovery
IEEE Transactions on Image Processing
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Image anomaly detection is the process of extracting a small number of clustered pixels which are different from the background. The type of image, its characteristics and the type of anomalies depend on the application at hand. In this paper, we introduce a new statistical model called noncausal autoregressive-autoregressive conditional heteroscedasticity (AR-ARCH) model for background in sonar images. Based on this background model, we propose a novel anomaly detection technique in sonar images. This new statistical model (i.e. noncausal ARCH) is an extension of the conventional ARCH model. We provide sufficient stationarity conditions and develop a computationally efficient method for estimating the model parameters which reduces to solving two sets of linear equations. We show that this estimator is asymptotically consistent. Using matched subspace detector (MSD) along with noncausal AR-ARCH modeling of the background in the wavelet domain, we propose an anomaly detection algorithm for sonar images, which is computationally efficient and less dependent on the image orientation. Simulation results demonstrate the performance of the proposed parameter estimation and the anomaly detection algorithm.