Relevant bibliographies by topics / RANSAC - RANdom Sample Consensus - Algorith

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'RANSAC - RANdom Sample Consensus - Algorith'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'RANSAC - RANdom Sample Consensus - Algorith.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "RANSAC - RANdom Sample Consensus - Algorith"

1

Toda, Yuichiro, and Naoyuki Kubota. "Evolution Strategy Sampling Consensus for Robust Estimator." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 5 (2016): 788–802. http://dx.doi.org/10.20965/jaciii.2016.p0788.

Full text
Abstract:
RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. Genetic Algorithm SAmple Consensus (GASAC) based on a population-based multi-point search was proposed in order to improve RANSAC. GASAC can improve the performance of search. However, it is sometimes difficult to main
APA, Harvard, Vancouver, ISO, and other styles
2

Ghergherehchi, Mitra, Yoon Sang Kim, Seung Yeol Kim, and Hossein Afarideh. "RANdom sample consensus (RANSAC) algorithm for enhancing overlapped etched track counting." IET Image Processing 9, no. 2 (2015): 97–106. http://dx.doi.org/10.1049/iet-ipr.2013.0885.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Sun, Qian, Ming Diao, Yibing Li, and Ya Zhang. "An improved binocular visual odometry algorithm based on the Random Sample Consensus in visual navigation systems." Industrial Robot: An International Journal 44, no. 4 (2017): 542–51. http://dx.doi.org/10.1108/ir-11-2016-0280.

Full text
Abstract:
Purpose The purpose of this paper is to propose a binocular visual odometry algorithm based on the Random Sample Consensus (RANSAC) in visual navigation systems. Design/methodology/approach The authors propose a novel binocular visual odometry algorithm based on features from accelerated segment test (FAST) extractor and an improved matching method based on the RANSAC. Firstly, features are detected by utilizing the FAST extractor. Secondly, the detected features are roughly matched by utilizing the distance ration of the nearest neighbor and the second nearest neighbor. Finally, wrong matched
APA, Harvard, Vancouver, ISO, and other styles
4

Zhou, Jun. "Epipolar Geometry Estimation Using Improved LO-RANSAC." Advanced Materials Research 213 (February 2011): 255–59. http://dx.doi.org/10.4028/www.scientific.net/amr.213.255.

Full text
Abstract:
The estimation of the epipolar geometry is of great interest for a number of computer vision and robotics tasks, and which is especially difficult when the putative correspondences include a low percentage of inliers correspondences or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for improve of locally optimized R
APA, Harvard, Vancouver, ISO, and other styles
5

Tian, Zhuo, and Bai Cheng Li. "Optimize Preview Model Parameters Evaluation of RANSAC." Applied Mechanics and Materials 687-691 (November 2014): 3984–87. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3984.

Full text
Abstract:
The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for optimizing the preview model parameters evaluation of RANSAC that has the benefit of offering fast and accurate RANSAC. With guaranteeing the same confidence of the solution as RANSAC, a very large number of erroneous model parameters obtained from the contaminated samples are discarded in the preview evaluation selection. And use local optimization step apply to
APA, Harvard, Vancouver, ISO, and other styles
6

Jin, Young-Hoon, and Won-Hyung Lee. "Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud." Applied Sciences 9, no. 5 (2019): 974. http://dx.doi.org/10.3390/app9050974.

Full text
Abstract:
In this paper, an algorithm is proposed that can perform cylinder type matching faster than the existing method in point clouds that represent space. The existing matching method uses Hough transform and completes the matching through preprocessing such as noise filtering, normal estimation, and segmentation. The proposed method completes the matching through the methodology of random sample consensus (RANSAC) and principal component analysis (PCA). Cylindrical pipe estimation is based on two mathematical models that compute the parameters and combine the results to predict spheres and lines.
APA, Harvard, Vancouver, ISO, and other styles
7

Li, X. G., C. Ren, T. X. Zhang, Z. L. Zhu, and Z. G. Zhang. "UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 67–70. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-67-2020.

Full text
Abstract:
Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimenta
APA, Harvard, Vancouver, ISO, and other styles
8

López-Martínez, Alan, and Francisco Javier Cuevas. "Multiple View Relations Using the Teaching and Learning-Based Optimization Algorithm." Computers 9, no. 4 (2020): 101. http://dx.doi.org/10.3390/computers9040101.

Full text
Abstract:
In computer vision, estimating geometric relations between two different views of the same scene has great importance due to its applications in 3D reconstruction, object recognition and digitization, image registration, pose retrieval, visual tracking and more. The Random Sample Consensus (RANSAC) is the most popular heuristic technique to tackle this problem. However, RANSAC-like algorithms present a drawback regarding either the tuning of the number of samples and the threshold error or the computational burden. To relief this problem, we propose an estimator based on a metaheuristic, the T
APA, Harvard, Vancouver, ISO, and other styles
9

Zhou, Jun. "Optimize Fundamental Matrix Estimation Based on RANSAC." Applied Mechanics and Materials 50-51 (February 2011): 333–37. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.333.

Full text
Abstract:
Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion, and which is especially difficult since it is often based on correspondences that are spoilt by noise and outliers. The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation, primarily due to its ability to tolerate a tremendous fraction of outliers. In this article, we provide an approach for improve of RANSAC that has the benefit of offering fast and accurate RANSAC, and combine the M-estimation algorithm get the fun
APA, Harvard, Vancouver, ISO, and other styles
10

Xu, Guangxuan, Yajun Pang, Zhenxu Bai, Yulei Wang, and Zhiwei Lu. "A Fast Point Clouds Registration Algorithm for Laser Scanners." Applied Sciences 11, no. 8 (2021): 3426. http://dx.doi.org/10.3390/app11083426.

Full text
Abstract:
Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterw
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "RANSAC - RANdom Sample Consensus - Algorith"

1

Zhao, Yue. "Biopsy needles localization and tracking methods in 3d medical ultrasound with ROI-RANSAC-KALMAN." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0015/document.

Full text
Abstract:
Dans les examens médicaux et les actes de thérapie, les techniques minimalement invasives sont de plus en plus utilisées. Des instruments comme des aiguilles de biopsie, ou des électrodes sont utilisés pour extraire des échantillons de cellules ou pour effectuer des traitements. Afin de réduire les traumatismes et de faciliter le suivi visuelle de ces interventions, des systèmes d’assistance par imagerie médicale, comme par exemple, par l’échographie 2D, sont utilisés dans la procédure chirurgicale. Nous proposons d’utiliser l’échographie 3D pour faciliter la visualisation de l’aiguille, mais
APA, Harvard, Vancouver, ISO, and other styles
2

Méler, Antoine. "BetaSAC et OABSAC, deux nouveaux 'echantillonnages conditionnels pour RANSAC." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00936650.

Full text
Abstract:
L'algorithme RANSAC est l'approche la plus commune pour l'estimation robuste des paramètres d'un modèle en vision par ordinateur. C'est principalement sa capacité à traiter des données contenant potentiellement plus d'erreurs que d'information utile qui fait son succès dans ce domaine où les capteurs fournissent une information très riche mais très difficilement exploitable. Depuis sa création, il y a trente ans, de nombreuses modifications ont été proposées pour améliorer sa vitesse, sa précision ou sa robustesse. Dans ce travail, nous proposons d'accélérer la résolution d'un problème par RAN
APA, Harvard, Vancouver, ISO, and other styles
3

Saravi, Sara. "Use of Coherent Point Drift in computer vision applications." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/12548.

Full text
Abstract:
This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The ide
APA, Harvard, Vancouver, ISO, and other styles
4

Γράψα, Ιωάννα. "Ανάπτυξη τεχνικών αντιστοίχισης εικόνων με χρήση σημείων κλειδιών". Thesis, 2012. http://hdl.handle.net/10889/5500.

Full text
Abstract:
Ένα σημαντικό πρόβλημα είναι η αντιστοίχιση εικόνων με σκοπό τη δημιουργία πανοράματος. Στην παρούσα εργασία έχουν χρησιμοποιηθεί αλγόριθμοι που βασίζονται στη χρήση σημείων κλειδιών. Αρχικά στην εργασία βρίσκονται σημεία κλειδιά για κάθε εικόνα που μένουν ανεπηρέαστα από τις αναμενόμενες παραμορφώσεις με την βοήθεια του αλγορίθμου SIFT (Scale Invariant Feature Transform). Έχοντας τελειώσει αυτή τη διαδικασία για όλες τις εικόνες, προσπαθούμε να βρούμε το πρώτο ζευγάρι εικόνων που θα ενωθεί. Για να δούμε αν δύο εικόνες μπορούν να ενωθούν, ακολουθεί ταίριασμα των σημείων κλειδιών τους. Όταν έ
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "RANSAC - RANdom Sample Consensus - Algorith"

1

Raguram, Rahul, Jan-Michael Frahm, and Marc Pollefeys. "A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88688-4_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Kai, Hongbo Li, and Zengqi Sun. "Ellipse Detection-Based Bin-Picking Visual Servoing System." In Engineering Creative Design in Robotics and Mechatronics. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4225-6.ch008.

Full text
Abstract:
In this chapter, the authors tackle the task of picking parts from a bin (bin-picking task), employing a 6-DOF manipulator on which a single hand-eye camera is mounted. The parts are some cylinders randomly stacked in the bin. A Quasi-Random Sample Consensus (Quasi-RANSAC) ellipse detection algorithm is developed to recognize the target objects. Then the detected targets’ position and posture are estimated utilizing camera’s pin-hole model in conjunction with target’s geometric model. After that, the target, which is the easiest one to pick for the manipulator, is selected from multi-detected results and tracked while the manipulator approaches it along a collision-free path, which is calculated in work space. At last, the detection accuracy and run-time performance of the Quasi-RANSAC algorithm is presented, and the final position of the end-effecter is measured to describe the accuracy of the proposed bin-picking visual servoing system.
APA, Harvard, Vancouver, ISO, and other styles
3

Xie, Fugui, Tonggang Zhang, Dan Zhong, and Yuhui Kan. "An Automatic Spherical Targets Detection Method with Multiple Geometrical Constraints." In Advances in Transdisciplinary Engineering. IOS Press, 2020. http://dx.doi.org/10.3233/atde200238.

Full text
Abstract:
Spherical targets are used extensively in the registration and coordinate transformation of the railway point cloud. Thus, it is necessary to accurately detect the spherical targets from the railway point cloud. This paper proposes an automatic spherical targets detection method with multiple geometrical constraints. In this method, possible spherical points are extracted by the improved three points filter method. And possible spherical points are refined according to neighborhood height difference and curvature. Then, the refined possible spherical points are spatially clustered by the Euclidean clustering method and the potential target point clouds can be extracted by constructing the spherical neighborhood according to the cluster centroid. Finally, the ratio constrained random sample consensus (RC-RANSAC) method is proposed in this paper, based on the RANSAC method, to detect the spherical targets in the potential target point clouds. The point cloud scanned from the high-speed railway is taken as experimental data. The spherical targets in the point cloud are detected by this method. The experimental results show that the proposed method can detect the spherical target with and without the background in radial direction.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "RANSAC - RANdom Sample Consensus - Algorith"

1

Duan, Jianyu, Lingyu Sun, Lijun Li, Zongmiao Dai, Zhenkai Xiong, and Jinxi Wang. "A Real-Time Image Matching Algorithm for Binocular Stereo Measurement System." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11185.

Full text
Abstract:
Abstract Binocular stereo measurement system can obtain accurate three-dimensional information from two-dimensional images. It has been widely applied in many fields such as vehicle tracking, robot navigating, automatic crane lifting, as well as other fields. The crucial step of binocular stereo measurement is image matching. For the image matching, it is a great challenge to ensure both real-time and matching accuracy simultaneously. The image matching algorithm has a great influence on the image matching time and accuracy. In this paper, a real-time image matching algorithm for binocular ste
APA, Harvard, Vancouver, ISO, and other styles
2

Yoo, Jewoo, Mohammed S. Mubarak, Roald van Borselen, and Constantine Tsingas. "Line-guided first break picking via random sample consensus (RANSAC)." In SEG Technical Program Expanded Abstracts 2020. Society of Exploration Geophysicists, 2020. http://dx.doi.org/10.1190/segam2020-3422645.1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Yoo, J., R. V. Borselen, M. S. Mubarak, and C. Tsingas. "Automated First Break Picking Method Using a Random Sample Consensus (RANSAC)." In 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201901195.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chang, Jaewon. "Robust needle recognition using Artificial Neural Network (ANN) and Random Sample Consensus (RANSAC)." In 2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2012). IEEE, 2012. http://dx.doi.org/10.1109/aipr.2012.6528219.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Safy, M., Guangming Shi, and A. S. Amein. "Semi-automatic image registration using harris corner detection and random sample consensus (RANSAC)." In IET International Radar Conference 2013. Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.0198.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Scherrer, Benoit, and Simon K. Warfield. "Retrospective local artefacts detection in diffusion-weighted images using the Random Sample Consensus (RANSAC) paradigm." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235606.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Misra, Indranil, S. Manthira Moorthi, Debajyoti Dhar, and R. Ramakrishnan. "An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model." In 2012 1st International Conference on Recent Advances in Information Technology (RAIT). IEEE, 2012. http://dx.doi.org/10.1109/rait.2012.6194482.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Shahabi, Ebrahim, Wei-Hao Lu, Po Ting Lin, and Chin-Hsing Kuo. "Computer Vision-Based Object Recognition and Automatic Pneumatic Soft Gripping." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97976.

Full text
Abstract:
Abstract During recent years, soft robotic is a new sub-class of the robots. Soft robotic has several engaging features, such as lightweight, low cost, simple fabrication, easy control, etc. Commercial products such as soft grippers are now available to apply in various fields and applications, for example, agriculture, medicine, machinery, etc. This paper proposes a novel method of grasping in soft robotic fields using computer vision to find the shape, size, and angle of the object to define the best type of grasping mode. Random Sample Consensus (RANSAC) was used to iteratively select rando
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'RANSAC - RANdom Sample Consensus - Algorith' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography