Results 121 to 130 of about 42,043 (166)

Gamma Mixture Models for Outlier Removal

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
In this paper, we introduce a probabilistic outlier model which is seamlessly integrated into machine learning frameworks (e.g., boosting and deep neural network) to accurately identify outliers in training samples. With two Gamma mixtures, the proposed model can estimate the distribution of inlier and outlier samples respectively and generates their ...
Xin Wu, Ling Cai, Rongrong Ji
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Optimal outlier removal in high-dimensional

Proceedings of the thirty-third annual ACM symposium on Theory of computing, 2001
We study the problem of finding an outlier-free subset of a set of points (or a probability distribution) in n-dimensional Euclidean space. A point x is defined to be a β-outlier if there exists some direction w in which its squared distance from the mean along w is greater than β times the average squared distance from the mean along w [1].
John Dunagan, Santosh Vempala
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k -means clustering with outlier removal

Pattern Recognition Letters, 2017
We study the problem of data clustering with outlier detection.We propose a k-means-type algorithm by incorporating an additional cluster into the objective function.The algorithm is able to provide data clustering and outlier detection simultaneously.Outliers are not used in the cluster center calculation.Experiments on synthetic and real data show ...
Guojun Gan, Michael Kwok-Po Ng
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Analytic outlier removal in line fitting

Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), 2002
The conventional ordinary least squares (OLS) method of fitting a line to a set of data points is very unreliable when the amount of random noise in the input (such as an image) is significant compared with the amount of data that is correlated with the lane itself. In this paper we present an analytic method of separating the data of interest from the
N.S. Netanyahu, I. Weiss
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k‐Nearest neighbors optimization‐based outlier removal

Journal of Computational Chemistry, 2014
Datasets of molecular compounds often contain outliers, that is, compounds which are different from the rest of the dataset. Outliers, while often interesting may affect data interpretation, model generation, and decisions making, and therefore, should be removed from the dataset prior to modeling efforts.
Abraham, Yosipof, Hanoch, Senderowitz
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Outlier removal through minimum spanning tree

Journal of Discrete Mathematical Sciences and Cryptography, 2012
Abstract Minimum spanning tree-based algorithm is capable of detecting outlier from data set having irregular shape, size and boundaries. Detecting outliers using Minimum Spanning Tree-based algorithm is a big desire. Outlier detection is an extremely an important task in a wide variety of application.
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A Multi-Objective Genetic Algorithm for Outlier Removal

Journal of Chemical Information and Modeling, 2015
Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set ...
Oren E, Nahum, Abraham, Yosipof, Hanoch, Senderowitz   +2 more
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Efficient outlier removal in vision based navigation

Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium, 2012
Vision based navigation algorithms estimate the position and attitude of a sensor platform by tracking stationary features in the neighboring environment across multiple image frames captured with an on-board camera. The set of feature matches between two frames is used to compute camera motion using algorithms based on multi-view geometry.
Yunqian Ma, Shrikant Rao
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Learning Discriminative Reconstructions for Unsupervised Outlier Removal

2015 IEEE International Conference on Computer Vision (ICCV), 2015
We study the problem of automatically removing outliers from noisy data, with application for removing outlier images from an image collection. We address this problem by utilizing the reconstruction errors of an autoencoder. We observe that when data are reconstructed from low-dimensional representations, the inliers and the outliers can be well ...
Yan Xia, Xudong Cao, Fang Wen, Gang Hua, Jian Sun   +4 more
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Robust image matching with cascaded outliers removal

Pattern Recognition and Image Analysis, 2017
Finding feature correspondences between a pair of images is a fundamental problem in computer vision for 3D reconstruction and target recognition. In practice, for feature based matching methods, there is often having a higher percentage of incorrect matches and decreasing the matching accuracy, which is not suitable for subsequent processing.
Jianfang Dou, Qin Qin, Zimei Tu
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