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Pre-processing of Retinal Images for Removal of Outliers
Wireless Personal Communications, 2020Early diagnosis of diseases related with retina such as glaucoma is of utmost importance in current scenario as it is the second most prevailing cause of irreversible blindness over the world and is expected to increase further in near future. It is commonly diagnosed using retinal images which are acquired by digital fundus cameras.
Niharika Thakur, Mamta Juneja
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Removing outliers by minimizing the sum of infeasibilities
Image and Vision Computing, 2010This paper shows that we can classify latent outliers efficiently through the process of minimizing the sum of infeasibilities (SOI). The SOI minimization has been developed in the area of convex optimization to find an initial solution, solve a feasibility problem, or check out some inconsistent constraints.
Hyunjung Lee +2 more
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Analytic outlier removal in line fitting
Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), 2002The 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
Nathan S. Netanyahu, Isaac Weiss
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Guaranteed Outlier Removal for Point Cloud Registration with Correspondences
An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers ...
Tat-Jun Chin, Alvaro PARRA
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Removing Outliers Using The L\infty Norm
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06), 2006Recently, there has been interest in solving geometric vision problems such as triangulation and camera resectioning using L\infty minimization. One key advantage of using the L\infty norm rather than the L2 norm is that the L\infty cost function has a single minimum unlike the commonly used L2 cost function which typically has multiple local minima ...
Kristy Sim, Richard I. Hartley
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A Multi-Objective Genetic Algorithm for Outlier Removal
Journal of Chemical Information and Modeling, 2015Quantitative 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 +2 more
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Point clouds, usually obtained through scanning or various image processing, are commonly affected by noise and outliers. Such artifacts compromise data quality as they significantly distort subsequent processes, such as normal estimation and surface reconstruction.
Marin, Diana +3 more
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Marin, Diana +3 more
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Outliers Removal and Consolidation of DYNAMIC Point Cloud
2018 25th IEEE International Conference on Image Processing (ICIP), 2018Recently, there has been increasing interest in the processing of dynamic scenes as captured by 3D scanners, ideally suited for challenging applications such as immersive tele-presence systems and gaming. Despite the fact that the resolution and accuracy of the modern 3D scanners are constantly improving, the captured 3D point clouds are usually noisy ...
Gerasimos Arvanitis +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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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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