Results 191 to 200 of about 30,930 (225)

Beyond RANSAC: User Independent Robust Regression

2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006
RANSAC is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use in a lot of applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important problem still remains.
R. Subbarao, P. Meer
openaire   +1 more source

RANSAC-SVM for large-scale datasets

2008 19th International Conference on Pattern Recognition, 2008
Support 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 Nishida, Takio Kurita
openaire   +1 more source

Robust vision-based displacement measurement and acceleration estimation using RANSAC and Kalman filter

Earthquake Engineering and Engineering Vibration, 2023
Jongbin Won, Jong-Woong Park, Minhyuk Song, Youn-sik Kim, D. Moon   +4 more
semanticscholar   +1 more source

RANSAC for (Quasi-)Degenerate data (QDEGSAC)

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06), 2006
The 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
J.-M. Frahm, M. Pollefeys
openaire   +1 more source

D-RANSAC: Distributed Robust Consensus

2015
“Robustness is the ability of a system to cope with errors during the execution.” This property is essential in any robotic system. A reliable robotic network must be able to fuse its perception of the world in a robust way. Data association mistakes and measurement errors are some of the factors that can contribute to an incorrect consensus value.
Eduardo Montijano, Carlos Sagüés
openaire   +1 more source

Hierarchical RANSAC for accurate horizon detection

2016 24th Mediterranean Conference on Control and Automation (MED), 2016
The 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, Bok-Suk Shin, Han Wang
openaire   +1 more source

RANSAC for outlier detection

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
openaire   +1 more source

Multiple target tracking using recursive RANSAC

2014 American Control Conference, 2014
Estimating the states of multiple dynamic targets is difficult due to noisy and spurious measurements, missed detections, and the interaction between multiple maneuvering targets. In this paper a novel algorithm, which we call the recursive random sample consensus (R-RANSAC) algorithm, is presented to robustly estimate the states of an unknown number ...
Peter C. Niedfeldt, Randal W. Beard
openaire   +1 more source

Automated detection of boundary line in paddy field using MobileV2-UNet and RANSAC

Computers and Electronics in Agriculture, 2022
Yong He, Xiya Zhang, Zeqing Zhang, H. Fang   +3 more
semanticscholar   +1 more source

Efficient RANSAC for Point‐Cloud Shape Detection

Computer graphics forum (Print), 2007
Ruwen Schnabel, Roland Wahl, Reinhard Klein   +2 more
semanticscholar   +1 more source