Results 1 to 10 of about 42,023 (146)
An efficient outlier removal method for scattered point cloud data. [PDF]
PLoS ONE, 2018 Outlier removal is a fundamental data processing task to ensure the quality of scanned point cloud data (PCD), which is becoming increasing important in industrial applications and reverse engineering.
Xiaojuan Ning +3 more
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Iterative Outlier Removal: A Method for Identifying Outliers in Laboratory Recalibration Studies. [PDF]
Clin Chem, 2016 AbstractBACKGROUNDExtreme values that arise for any reason, including those through nonlaboratory measurement procedure-related processes (inadequate mixing, evaporation, mislabeling), lead to outliers and inflate errors in recalibration studies. We present an approach termed iterative outlier removal (IOR) for identifying such outliers.METHODSWe ...
Parrinello CM +8 more
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Neural Network-Based Stereo Vision Outlier Removal [PDF]
MATEC Web of Conferences, 2022 Stereo vision systems rely on accurate feature matching to provide valid stereo reconstruction and pose estimation. This accuracy is achieved through outlier removal techniques, such as RANSAC. However, images also contain semantic information, which can
Strauss March, van Daalen Corné E.
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Improved Ellipse Fitting Algorithm with Outlier Removal [PDF]
Jisuanji kexue, 2022 The results of ellipse fitting can be considerably distorted by outliers in the fitted point set.To tackle this problem, three improved ellipse fitting algorithms, one of which is based on least trimmed square, and the other two on dual point removal ...
GUO Si-yu, WU Yan-dong
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Clustering With Outlier Removal [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Cluster analysis and outlier detection are strongly coupled tasks in data mining area. Cluster structure can be easily destroyed by few outliers; on the contrary, outliers are defined by the concept of cluster, which are recognized as the points belonging to none of the clusters.
Hongfu Liu, Jun Li, Yue Wu, Yun Fu
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Heteroscedasticity testing after outlier removal [PDF]
Econometric Reviews, 2020 Given the effect that outliers can have on regression and specification testing, a vastly used robustification strategy by practitioners consists in: (i) starting the empirical analysis with an outlier detection procedure to deselect atypical data values; then (ii) continuing the analysis with the selected non-outlying observations.
Berenguer-Rico, V, Wilms, I
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CAISOV: Collinear Affine Invariance and Scale-Orientation Voting for Reliable Feature Matching
Remote Sensing, 2022 Reliable feature matching plays an important role in the fields of computer vision and photogrammetry. Due to the complex transformation model caused by photometric and geometric deformations, and the limited discriminative power of local feature ...
Haihan Luo +5 more
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STAR_outliers: a python package that separates univariate outliers from non-normal distributions
BioData Mining, 2023 There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers.
John T. Gregg, Jason H. Moore
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Outlier removal using duality [PDF]
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010 In this paper we consider the problem of outlier removal for large scale multiview reconstruction problems. An efficient and very popular method for this task is RANSAC. However, as RANSAC only works on a subset of the images, mismatches in longer point tracks may go undetected. To deal with this problem we would like to have, as a post processing step
Olsson, Carl +2 more
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Journal of Medical Internet Research, 2023 BackgroundReference intervals (RIs) play an important role in clinical decision-making. However, due to the time, labor, and financial costs involved in establishing RIs using direct means, the use of indirect methods, based on ...
Dan Yang +10 more
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