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Distributed RANSAC for 3D reconstruction
SPIE Proceedings, 2008Many low or middle level 3D reconstruction algorithms involve a robust estimation and selection step by which parameters of the best model are estimated and inliers fitting this model are selected. The RANSAC algorithm is the most widely used robust algorithm for this step. However, this robust algorithm is computationally demanding.
Mai Xu, Maria Petrou
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Radar odometry with recursive-RANSAC
IEEE Transactions on Aerospace and Electronic Systems, 2016This paper explores the use of radar odometry for Global Positioning System–denied navigation. The range progression from arbitrary ground-based point scatterers is used to estimate an unmanned aerial vehicle’s relative motion. In high clutter environments, the recursive-RANSAC algorithm provides robust and efficient feature identification, data ...
Eric B. Quist +2 more
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Performance Evaluation of RANSAC Family
Procedings of the British Machine Vision Conference 2009, 2009RANSAC (Random Sample Consensus) has been popular in regression problem with samples contaminated with outliers. It has been a milestone of many researches on robust estimators, but there are a few survey and performance analysis on them. This paper categorizes them on their objectives: being accurate, being fast, and being robust.
Sunglok Choi, Taemin Kim, Wonpil Yu
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Order Statistics of RANSAC and Their Practical Application
International Journal of Computer Vision, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Evren Imre, Adrian Hilton 0001
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DL-RANSAC: An Improved RANSAC with Modified Sampling Strategy Based on the Likelihood
2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), 2019This paper intends to improve the RANSAC by introducing new sampling method which provides prior knowledge of random population before selecting the hypothesis set. In traditional RANSAC algorithm, a minimal set of samples are chosen randomly from the population containing uneven noise and iteration continues until the desired model is found. Ambiguity
Miftahur Rahman, Xueyuan Li, Xufeng Yin
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Hierarchical RANSAC-Based Rotation Averaging
IEEE Signal Processing Letters, 2020In this letter, we present a novel rotation averaging pipeline, which is performed in a hierarchical manner. Unlike the traditional rotation averaging methods which focus on designing robust loss function to get rid of the impacts of the relative rotation outliers, here the outliers are detected and filtered by leveraging the well-known robust model ...
Xiang Gao 0009 +3 more
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Image denoising using RANSAC and compressive sensing
Multimedia Tools and Applications, 2022Image denoising is a vital image processing phase aiming to improve the quality of images and to make them more informative. In this paper, we propose a blind denoising approach for removing the outliers (impulsive disturbances) from digital images, by combining the random sample consensus (RANSAC) and compressive sensing (CS) principles.
Isidora Stankovic +4 more
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DT-RANSAC: A Delaunay Triangulation Based Scheme for Improved RANSAC Feature Matching
2013The main objective in content-based image retrieval is to find images similar to a query image in an image collection. Matching using descriptors computed from regions centered at local invariant interest points (keypoints) have become popular because of their robustness to changes in viewpoint and occlusion.
Priyadarshi Bhattacharya +1 more
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A probabilistic analysis of a common RANSAC heuristic
Machine Vision and Applications, 2018Random Sample Consensus (RANSAC) is an iterative algorithm for robust model parameter estimation from observed data in the presence of outliers. First proposed by Fischler and Bolles back in 1981, it still is a very popular algorithm in the computer vision community.
Hemanth Kumar Sangappa +1 more
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RANSAC-SVM for large-scale datasets
2008 19th International Conference on Pattern Recognition, 2008Support Vector Machines (SVMs), though accurate, are still difficult to solve large-scale applications, due to the computational and storage requirement. To relieve this problem, we propose RANSAC-SVM method, which trains a number of small SVMs for randomly selected subsets of training set, while tuning their parameters to fit SVMs to whole training ...
Kenji Watanabe, Takio Kurita
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