Two dimensional noncausal AR-ARCH model: Stationary conditions, parameter estimation and its application to anomaly detection
Two dimensional noncausal AR-ARCH model: Stationary conditions, parameter estimation and its application to anomaly detection
复制标题
二维非因果 AR-ARCH 模型：稳态条件、参数估计及其在异常检测中的应用
DOI：
10.1016/j.sigpro.2013.12.003
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
I. Cohen
中科院分区：
文献类型：
--
作者：
S. Mousazadeh;I. Cohen
图像异常检测是指从图像中提取出与背景不同的少量聚类像素的过程。图像的类型、特征和异常类型取决于当前的应用。本文提出了一种新的声纳图像背景统计模型--非因果自回归-自回归条件异方差（AR-AR）模型。基于此背景模型，我们提出了一种新的异常检测技术在声纳图像。这种新的统计模型（即非因果模型）是传统的非因果模型的扩展。我们提供了充分的平稳性条件，并开发了一个计算效率高的方法来估计模型参数，减少到求解两组线性方程组。我们证明了这个估计量是渐近相合的。利用匹配子空间检测器（MSD）沿着结合小波域背景的非因果AR模型，提出了一种计算量小且对图像方向依赖性小的声纳图像异常检测算法。仿真结果验证了所提出的参数估计和异常检测算法的性能。
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.