Results 11 to 20 of about 8,729 (215)
Ransac for outlier detection [PDF]
Geodesy and Cartography, 2012 Up‐to‐date digital photogrammetry involves operations on huge data sets, and with classical image processing procedures it might be time consuming to find out the best solution.
Birutė Ruzgienė, Wolfgang Förstner
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Generalized Differentiable RANSAC [PDF]
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 We propose $\nabla$-RANSAC, a generalized differentiable RANSAC that allows learning the entire randomized robust estimation pipeline. The proposed approach enables the use of relaxation techniques for estimating the gradients in the sampling distribution, which are then propagated through a differentiable solver.
Tong Wei 0002 +4 more
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Space-Partitioning RANSAC [PDF]
, 2022 A new algorithm is proposed to accelerate RANSAC model quality calculations. The method is based on partitioning the joint correspondence space, e.g., 2D-2D point correspondences, into a pair of regular grids. The grid cells are mapped by minimal sample models, estimated within RANSAC, to reject correspondences that are inconsistent with the model ...
Daniel Barath, Gábor Valasek
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Optimal Randomized RANSAC [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008 A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-specified probability. The solution is found in time that is (i) close to the shortest possible and (ii) superior to any deterministic verification strategy.
Ondrej Chum, Jiri Matas
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CoRR, 2023 RANSAC and its variants are widely used for robust estimation, however, they commonly follow a greedy approach to finding the highest scoring model while ignoring other model hypotheses. In contrast, Iteratively Reweighted Least Squares (IRLS) techniques gradually approach the model by iteratively updating the weight of each correspondence based on the
Luca Cavalli +3 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2015 A homography matrix is used in computer vision field to solve the correspondence problem between a pair of stereo images. RANSAC algorithm is often used to calculate the homography matrix by randomly selecting a set of features iteratively. CS-RANSAC algorithm in this paper converts RANSAC algorithm into two-layers.
GeunSik Jo +4 more
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RANSAC for Robotic Applications: A Survey
Sensors, 2022 Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of ...
José María Martínez-Otzeta +3 more
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SPIE Proceedings, 2006 In this paper, the classical RANSAC approach is considered for robust matching to remove mismatches (outliers) in a list of putative correspondences. We will examine the justification for using the minimal size of sample set in a RANSAC trial and propose that the size of the sample set should be varied dynamically depending on the noise and data set ...
Hung, YS, Sze, WF, Tang, WK
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Randomized RANSAC with T(d,d) test [PDF]
Procedings of the British Machine Vision Conference 2002, 2002 Abstract Many computer vision algorithms include a robust estimation step where model parameters are computed from a data set containing a significant proportion of outliers. The ransac algorithm is possibly the most widely used robust estimator in the field of computer vision.
Jiri Matas, Ondrej Chum
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RANSAC based robust localization algorithm for visual sensor network [PDF]
Tongxin xuebao, 2013 Due to node failures or environmental changes,observed data on the target will be error in visual sensor network,so the least squares based multi-vision localization algorithm won’t be accurate.A centralized RANSAC based robust localization method was ...
Bo ZHANG +3 more
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