Results 11 to 20 of about 48,550 (262)
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]
Clinical Chemistry, 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 ...
Christina M, Parrinello +8 more
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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
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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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IEEE Access, 2022 The software engineering researchers have worked on different dimensions to facilitate better software effort estimates, including those focusing on dataset quality improvement.
Swarnima Singh Gautam, Vrijendra Singh
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SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data.
A. A. Matkan +4 more
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NEAR: An artifact removal pipeline for human newborn EEG data
Developmental Cognitive Neuroscience, 2022 Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging.
Velu Prabhakar Kumaravel +3 more
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Outliers May Not Be Automatically Removed
Journal of Experimental Psychology: General, 2022 Researchers often remove outliers when comparing groups. It is well documented that the common practice of removing outliers within groups leads to inflated type I error rates. However, it was recently argued by André that if outliers are instead removed across groups, type I error rates are not inflated. The same study discusses that removing outliers
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SUPERVISED OUTLIER DETECTION IN LARGE-SCALE MVS POINT CLOUDS FOR 3D CITY MODELING APPLICATIONS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 We propose to use a discriminative classifier for outlier detection in large-scale point clouds of cities generated via multi-view stereo (MVS) from densely acquired images.
C. Stucker +3 more
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A Geometric Estimation Technique Based on Adaptive M-Estimators: Algorithm and Applications
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Robust fitting is a basic technique and has been widely applied in photogrammetry and remote sensing, such as geometric correction. As known, typical robust estimators (include M-estimators, S-estimators, MM-estimators, etc.) often fail when outlier rate
Jiayuan Li +3 more
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