Results 1 to 10 of about 46,215 (298)
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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A Geometrical–Statistical Approach to Outlier Removal for TDOA Measurements [PDF]
IEEE Transactions on Signal Processing, 2017 30 pages, 10 figure, 3 tables, in press on IEEE Transactions on Signal ...
Marco Compagnoni +2 more
exaly +6 more sources
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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An Adversarial Optimization Approach to Efficient Outlier Removal [PDF]
Journal of Mathematical Imaging and Vision, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jin Yu 0001 +3 more
core +10 more sources
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, Yun Fu
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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
Carl Olsson +2 more
openaire +5 more sources
FOR: Point Cloud Outlier Removal Based on Fuzzy Theory and Informativeness and Its Application to 3D Object Detection [PDF]
SensorsLiDAR is widely used in autonomous driving. Although LiDAR point cloud data can provide stable and reliable information about the environment, it also faces the problem of a huge amount of data.
Lili Gan +4 more
doaj +2 more sources
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
europepmc +4 more sources
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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Valid Inference Corrected for Outlier Removal [PDF]
Journal of Computational and Graphical Statistics, 2019 21 pages, 6 figures, 2 ...
Shuxiao Chen, Jacob Bien
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