Topic
Mahalanobis distance
About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The proposed alignment framework for multimodal HR images not only can align the different multimodale data more accurately than existing state-of-the-art domain adaptation methods, but also has a fast and simple procedure for large-scale data situation which is caused by HR imaging.
Abstract: High-resolution (HR) remote sensing (RS) imaging opens the door to very accurate geometrical analysis for objects. However, it is difficult to simultaneous use massive HR RS images in practical applications, because these HR images are often collected in different multimodal conditions (multisource, multiarea, multitemporal, multiresolution, and multiangular) and learning method trained for one situation is difficult to use for others. The key problem is how to simultaneously tackle three main problems: spectral drift, spatial deformation, and band inconsistency. To deal with these problems, we propose an unsupervised tensorized principal component alignment framework in this paper. In this framework, local spatial–spectral patch data are used as basic units in order to achieve simultaneously multidimensional alignment. This framework seeks a domain-invariant tensor feature space by learning multilinear mapping functions which align the source tensor subspace with the target tensor subspace on different dimensions. In addition, an approach based on the Mahalanobis distance for dimensionality estimation of tensor subspace is proposed to determine best sizes of the aligned tensor subspace for reducing computational complexity. HR images from GF-1, GF-2, DEIMOS-2, WorldView-2, and WorldView-3 satellites are used to evaluate the performance. The experimental results show the following two points: first, the proposed alignment framework for multimodal HR images not only can align the different multimodal data more accurately than existing state-of-the-art domain adaptation methods, but also has a fast and simple procedure for large-scale data situation which is caused by HR imaging. Second, the proposed tensor dimensionality estimation method is an efficient technology for seeking the intrinsic dimensions of high-order data.
28 citations
••
TL;DR: This paper compares the Gaussian distribution, which is based on the Mahalanobis distance and a Choquet integral based distance, and introduces an operator that generalizes theChoquet integral and theMahalanobIS distance, the Choquet-MahalanOBis integral.
28 citations
••
18 Nov 2008TL;DR: In this paper, random sample consensus (RANSAC) is used to estimate 3D line from the3D point set, the Mahalanobis distance from each 3D point to the 3Dline is derived, and the statistically motivated distance measure is usedto compute the support for the detected 3D lines.
Abstract: The paper describes a robust method to extract 3D lines from stereo point clouds. This method combines 2D image information with 3D point clouds from a stereo camera. 2D lines are first extracted from the image in the stereo pair, followed by 3D line regression from the back-projected 3D point set of the images points in the detected 2D lines. In this paper, random sample consensus (RANSAC) is used to estimate 3D line from the 3D point set, the Mahalanobis distance from each 3D point to the 3D line is derived, and the statistically motivated distance measure is used to compute the support for the detected 3D line. Experimental results on real environment with high level of clutter, occlusion, and noise demonstrate the robustness of the algorithm.
28 citations
••
TL;DR: The effect of model updating on the identification of a pharmaceutical excipient based on its near-infrared (NIR) spectra has been investigated and a pragmatic updating approach was applied.
Abstract: The effect of model updating on the identification of a pharmaceutical excipient based on its near-infrared (NIR) spectra has been investigated. A pragmatic updating approach, consisting of adding stepwise newly available samples to the training set and rebuilding the classification model, was applied. Its performance is compared for three pattern recognition methods: the wavelength distance method, the Mahalanobis distance method, and the SIMCA (soft independent modeling of class analogy) residual variance method. For the wavelength distance method, the updating approach is straightforward. In the case of the multivariate classification methods, which are based on a certain number of significant principal components (PCs), the selection of the number of PCs included in the model must be performed with care, as this number has a major impact on the classification results.
28 citations
••
TL;DR: The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier.
28 citations