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RANSAC for (Quasi-)Degenerate data (QDEGSAC)
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06), 2006The computation of relations from a number of potential matches is a major task in computer vision. Often RANSAC is employed for the robust computation of relations such as the fundamental matrix. For (quasi-)degenerate data however, it often fails to compute the correct relation. The computed relation is always consistent with the data but RANSAC does
Jan-Michael Frahm, Marc Pollefeys
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Improving RANSAC for fast landmark recognition
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008We introduce a procedure for recognizing and locating planar landmarks for mobile robot navigation, based in the detection and recognition of a set of interest points. We use RANSAC for fitting a homography and locating the landmark. Our main contribution is the introduction of a geometrical constraint that reduces the number of RANSAC iterations by ...
Pablo Márquez-Neila +3 more
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Hierarchical RANSAC for accurate horizon detection
2016 24th Mediterranean Conference on Control and Automation (MED), 2016The horizon in marine scenes provides an important prior feature for unmanned surface vehicles (USV) based research and applications. However, most of existing research in horizon detection usually consider specific or simple scenarios. In this paper, we propose a novel approach to detect the horizon in maritime images with various situations by ...
Xiaozheng Mou +2 more
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2011
Random Sample Consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from random minimal data subsets and evaluating their validity from the support of the whole data.
Javier Civera +2 more
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Random Sample Consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from random minimal data subsets and evaluating their validity from the support of the whole data.
Javier Civera +2 more
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Mobility Fitting using 4D RANSAC
Computer Graphics Forum, 2016AbstractCapturing the dynamics of articulated models is becoming increasingly important. Dynamics, better than geometry, encode the functional information of articulated objects such as humans, robots and mechanics. Acquired dynamic data is noisy, sparse, and temporarily incoherent.
Hao Li 0015 +5 more
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RANSAC matching: Simultaneous registration and segmentation
2010 IEEE International Conference on Robotics and Automation, 2010The iterative closest points (ICP) algorithm is widely used for ego-motion estimation in robotics, but subject to bias in the presence of outliers. We propose a random sample consensus (RANSAC) based algorithm to simultaneously achieving robust and realtime ego-motion estimation, and multi-scale segmentation in environments with rapid changes.
Shao-Wen Yang +2 more
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MC-RANSAC: A Pre-processing Model for RANSAC using Monte Carlo method implemented on a GPU
2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013RANSAC is a repeating hypothesize-and-verify procedure for parameter estimation and filtering of noise or outlier data. In the traditional approach, this algorithm is evaluated without any prior information on the set of data points which leads to an increase in the number of iterations and compute time.
Priyank Trivedi +2 more
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A WD-RANSAC Instantaneous Frequency Estimator
IEEE Signal Processing Letters, 2016A Wigner distribution-based random sample consensus (RANSAC) algorithm for the instantaneous frequency estimation is proposed. The algorithm performance is studied for several signal types and compared with the state-of-the-art Viterbi algorithm in both accuracy and complexity.
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2010
Lietuviška santrauka. Nūdienos skaitmeninė fotogrametrija nagrinėja fotografinių vaizdų, kuriuose gausu duomenų, apdorojimo procedūras, todėl automatiškai rasti geriausią sprendimą ilgai trunka, būtina talpi kompiuterinė atmintis. Atliekant fotonuotraukų sugretinimą (matching), vienas iš pagrindinių uždavinių yra teisingai identifikuoti duomenų ...
Ruzgienė, Birutė, Fröhner, Wolfgang
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Lietuviška santrauka. Nūdienos skaitmeninė fotogrametrija nagrinėja fotografinių vaizdų, kuriuose gausu duomenų, apdorojimo procedūras, todėl automatiškai rasti geriausią sprendimą ilgai trunka, būtina talpi kompiuterinė atmintis. Atliekant fotonuotraukų sugretinimą (matching), vienas iš pagrindinių uždavinių yra teisingai identifikuoti duomenų ...
Ruzgienė, Birutė, Fröhner, Wolfgang
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