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Learning Discriminative Reconstructions for Unsupervised Outlier Removal
2015 IEEE International Conference on Computer Vision (ICCV), 2015We 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 +4 more
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A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Grouped outlier removal for robust ellipse fitting
2015 14th IAPR International Conference on Machine Vision Applications (MVA), 2015This paper presents a novel outlier removal method which is capable of fitting ellipse in real-time under high outlier rate, based on the phenomenon that outliers generated by ellipse edge point detector are likely to appear as groups due to real-world nuisances, such as under partial occlusion or illumination change.
Mang Shao +2 more
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Outlier Removal in Stereo Reconstruction of Orbital Images
2010NASA has recently been building 3-dimensional models of the moon based on photos taken from orbiting satellites and the Apollo missions. One issue with the stereo reconstruction is the handling of "outliers", or areas with rapid and unexpected change in the data.
Marvin Smith, Ara Nefian
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Guaranteed Outlier Removal with Mixed Integer Linear Programs
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the problem size. Although randomized algorithms are much more efficient, the optimality of the solution is not guaranteed.
Tat-Jun Chin +3 more
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Removing spatial outliers in PS applications
2012 International Conference on Selected Topics in Mobile and Wireless Networking, 2012In this paper we study the problem of sensor data verification in Participatory Sensing (PS) systems using an air quality/pollution monitoring application as a validation example. Data verification, in the context of PS, consists of the process of removing spatial outliers to properly reconstruct the variables of interest.
Diego Mendez, Miguel A. Labrador
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Heartbeat Selection Based on Outlier Removal
2022Miguel Carvalho, Susana Brás
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Outlier Removal for Fingerprinting Localization Methods
2022 56th Asilomar Conference on Signals, Systems, and Computers, 2022Brent Laird, Trac Tran
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Removing Outliers in Illumination Estimation
Color and Imaging Conference, 2012Brian V. Funt, Milan Mosny
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Improving recommendation quality through outlier removal
International Journal of Machine Learning and Cybernetics, 2022Yuanyuan Xu 0003 +2 more
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