Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

Algorithm of Imagined Left-Right Hand Movement Classification Based on Wavelet Transform and AR Parameter Model

Abstract: Brain-computer interface (BCI) provides new communication and control channels that do not depend on the brain's normal output of peripheral nerves and muscles. In this paper, we report on results of developing a single trial online motor imagery feature extraction method for BCI. The wavelet coefficients and autoregressive parameter model was used to extraction the features from the motor imagery EEG and the linear discriminant analysis based on mahalanobis distance was utilized to classify the pattern of left and right hand movement imagery. The performance was tested by the Graz dataset for BCI competition 2003 and satisfactory results are obtained with an error rate as low as 10.0%.

How Different is Different? Criterion and Sensitivity in Face-Space

Abstract: Not all detectable differences between face images correspond to a change in identity. Here we measure both sensitivity to change and the criterion difference that is perceived as a change in identity. Both measures are used to test between possible similarity metrics. Using a same/different task and the method of constant stimuli criterion is specified as the 50% “different” point (P50) and sensitivity as the Difference Limen (DL). Stimuli and differences are defined within a “face-space” based on Principal Components Analysis (PCA) of measured differences in three-dimensional shape. In Experiment 1 we varied views available. Criterion (P50) was lowest for identical full-face view comparisons that can be based on image differences. When comparing across views P50, was the same for a static 45o change as for multiple animated views, although sensitivity (DL) was higher for the animated case, where it was as high as for identical views. Experiments 2 and 3 tested possible similarity metrics. Experiment 2 contrasted Euclidean and Mahalanobis distance by setting PC1 or PC2 to zero. DL did not differ between conditions consistent with Mahalanobis. P50 was lower when PC2 changed emphasising that perceived changes in identity are not determined by the magnitude of Euclidean physical differences. Experiment 3 contrasted a distance with an angle based similarity measure. We varied the distinctiveness of the faces being compared by varying distance from the origin, a manipulation that affects distances but not angles between faces. Angular P50 and DL were both constant for faces from 1 to 2 standard deviations from the mean, consistent with an angular measure. We conclude that both criterion and sensitivity need to be considered and that an angular similarity metric based on standardised PC values provides the best metric for specifying what physical differences will be perceived to change in identity.

Egomotion And The Stabilized World

Abstract: This paper formulates the tasks of moving-object detection and motion-based depth recovery as a problem of sensor fusion in the presence of uncertainty. We utilize two sensor systems, one providing information about the local image velocity (spsecifying a point in image-velocity space), and the othter providing information about the camera motion (specifying a line segment in image-velocity space). 1Ne utilize Mahalanobis distance as a threshold rule for determining consistency between the measurements from the two sensor systems. We also suggest a framework for using the resulting segmented flow field to update estimates of the egomotion parameters.

Decision fusion for dual-window-based hyperspectral anomaly detector

Abstract: In hyperspectral anomaly detection, the dual-window-based detector is a widely used technique that employs two windows to capture nonstationary statistics of anomalies and back- ground. However, its detection performance is usually sensitive to the choice of window sizes and suffers from inappropriate window settings. In this work, a decision-fusion approach is pro- posed to alleviate such sensitivity by merging the results from multiple detectors with different window sizes. The proposed approach is compared with the classic Reed-Xiaoli (RX) algorithm as well as kernel RX (KRX) using two real hyperspectral data. Experimental results demonstrate that it outperforms the existing detectors, such as RX, KRX, and multiple-window-based RX. The overall detection framework is suitable for parallel computing, which can greatly reduce computational time when processing large-scale remote sensing image data. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or repro- duction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.9.097297) tional probability density functions under the two hypotheses (without and with anomaly) are assumed to be Gaussian. The solution turns out to be an adaptive Mahalanobis distance between the pixel under test and the local background. It is preferred to use local background to capture nonstationary statistics, and its advantage of using a global background covariance matrix has been demonstrated in the literature. 11-13 The RX detector has become the benchmark of anomaly detection algorithms in HSI. Obviously, the key to success is an appropriate estimate of a local background covariance matrix for effective background suppression. An adaptive RX detector employs a dual-window strategy: the inner window is slightly larger than the pixel size, the outer window is even larger than the inner one, and only the samples in the outer region (i.e., between the frames of inner and outer windows) are used to estimate the background covariance matrix to avoid the use of the potential anomalous pixels. Intuitively, the number of pixels in the outer region (related to the sizes of inner and outer windows) should be more than the number of bands so that the resulting covariance matrix can be full-rank for inverse matrix operation. However, even when the covariance matrix is ill-rank, its inversion can still be computed by several strategies, such

Health Assessment of Electronic Products using Mahalanobis Distance and Projection Pursuit Analysis

Abstract: With increasing complexity in electronic systems there is a need for system level anomaly detection and fault isolation. Anomaly detection based on vector similarity to a training set is used in this paper through two approaches, one the preserves the original information, Mahalanobis Distance (MD), and the other that compresses the data into its principal components, Projection Pursuit Analysis. These methods have been used to detect deviations in system performance from normal operation and for critical parameter isolation in multivariate environments. The study evaluates the detection capability of each approach on a set of test data with known faults against a baseline set of data representative of such “healthy” systems.. Keywords—Mahalanobis distance, Principle components, Projection pursuit, Health assessment, Anomaly.