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Mahalanobis distance

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


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01 Jan 1999
TL;DR: In this paper, a model has been developed to relate the size of a ship and the angle between waterline and horizon in image coordinates, to the real-life size and aspect angle of the ship, using the camera elevation and distance to the ship.
Abstract: The aim of the research presented in this paper is to find out whether automatic classification of ships from Forward Looking InfraRed (FLIR) images is feasible in maritime patrol aircraft. An image processing system has been developed for this task. It includes iterative shading correction and a top hat filter for the detection of the ship. It uses a segmentation algorithm based on the gray value distribution of the waves and the Hough transform to locate the waterline of the ship. A model has been developed to relate the size of the ship and the angle between waterline and horizon in image coordinates, to the real-life size and aspect angle of the ship. The model uses the camera elevation and distance to the ship. A data set was used consisting of two civil ships and four different frigates under different aspect angles and distances. From each of these ship images, 32 features were calculated, among which are the apparent size, the location of the hot spot and of the superstructures of the ship, and moment invariant functions. All features were used in feature selection processing using both the Mahalanobis and nearest neighbor (NN) criteria in forward, backward, and branch & bound feature selection procedures, to find the most significant features. Classification has been performed using a k-NN, a linear, and a quadratic classifier. In particular, using the 1-NN classifier, good results were achieved using a two-step classification algorithm.

44 citations

Journal ArticleDOI
TL;DR: A new method for data association in multitarget tracking in the framework of evidence theory based on the use of belief function in the sense of Dempster-Shafer theory of evidence using a normalized Mahalanobis distance.
Abstract: The problem of multitarget tracking in clutter has been shown to be very challenging for both the measurement of track association and the estimation of the targets' state. Several approaches have been suggested to solve this problem. In this paper, we present a new method for data association in multitarget tracking in the framework of evidence theory. The representation and the fusion of the information in our method are based on the use of belief function in the sense of Dempster-Shafer theory of evidence. The proposal introduces a method of computing mass assignment using a normalized Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses, the method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases using Monte Carlo simulations. The results demonstrate the feasibility of the proposal, and show improved performance compared to the aforementioned alternative commonly used methods.

44 citations

Book ChapterDOI
11 Oct 1998
TL;DR: A generic framework for pose estimation from geometric features is provided and more particularly two algorithms: a gradient descent on the Riemannian least squares distance and on the Mahalanobis distance are proposed.
Abstract: We provide in this article a generic framework for pose estimation from geometric features. We propose more particularly two algorithms: a gradient descent on the Riemannian least squares distance and on the Mahalanobis distance. For each method, we provide a way to compute the uncertainty of the resulting transformation. The analysis and comparison of the algorithms show their advantages and drawbacks and point out the very good prediction on the transformation accuracy. An application in medical image analysis validates the uncertainty estimation on real data and demonstrates that, using adapted and rigorous tools, we can detect very small modifications in medical images. We believe that these algorithms could be easily embedded in many applications and provide a thorough basis for computing many image statistics.

44 citations

Journal ArticleDOI
TL;DR: In this article, a new analysis technique is developed that fully takes into account the uncertainty ellipsoid for each measurement point, which solves for the positions and orientations of the measurement systems while determining the optimal fit to each physical point.
Abstract: Precision three-dimensional metrology frequently involves the measurement of common points by several three-dimensional measurement systems. These can be laser tracking interferometers, electronic theodolites, etc. A new analysis technique has been developed that fully takes into account the uncertainty ellipsoid for each measurement point. This technique solves for the positions and orientations of the measurement systems while determining the optimal fit to each physical point. No a priori knowledge of the location and orientation of the measurement systems is required. An initial estimate for the location and orientation of the measurement systems is derived from the measurement data by assuming equal uncertainties for each data point. Then a merit function is minimized to determine the optimized location and orientation of the measurement systems and the weighted mean for the position estimates of the physical points. This merit function is based on the Mahalanobis distance from multivariate statistics and takes into account the particular shape and orientation of the three-dimensional uncertainty ellipsoid of each data point from each measurement system. This technique can utilize data from differing types of three-dimensional measurement systems including distance only and angle only measurements, evaluate the “strength” of a measurement configuration and accommodate missing data points from some of the measurement systems.

44 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023208
2022452
2021232
2020239
2019249