Results 161 to 170 of about 9,523 (214)
GL-VSLAM: A General Lightweight Visual SLAM Approach for RGB-D and Stereo Cameras. [PDF]
Sensors (Basel)Li X +5 more
europepmc +1 more source
Semi-Supervised Deep Image Stitching for Moving Elongated Objects. [PDF]
Sensors (Basel)Lai X, Xie Z, Liu X.
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Research on LiDAR-Assisted Optimization Algorithm for Terrain-Aided Navigation of eVTOL. [PDF]
Sensors (Basel)Zhang G, Zhou J, Duan Z, Zhao W.
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Vision and 2D LiDAR Fusion-Based Navigation Line Extraction for Autonomous Agricultural Robots in Dense Pomegranate Orchards. [PDF]
Sensors (Basel)Shi Z +8 more
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Targetless Radar-Camera Calibration via Trajectory Alignment. [PDF]
Sensors (Basel)Durmaz O, Cevikalp H.
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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.
Ondřej Chum, Jiří Matas
exaly +6 more sources
LiDAR Filtering in 3D Object Detection Based on Improved RANSAC
Remote Sensing, 2022 At present, the LiDAR ground filtering technology is very mature. There are fewer applications in 3D-object detection due to the limitations of filtering accuracy and efficiency.
Wang Bingxu, Jinhui Lan
exaly +2 more sources
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in short, is introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local optimization (LO) step which is applied when a so-far-the-best model is found. The proposed LO step is conceptually simple, easy to implement, globally optimal and efficient. GC-
Daniel Barath, Jiří Matas
exaly +5 more sources
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SC-RANSAC: Spatial consistency on RANSAC
Multimedia Tools and Applications, 2018The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field,
Mehran Fotouhi +3 more
openaire +1 more source
2003
A new enhancement of ransac, the locally optimized ransac (lo-ransac), is introduced. It has been observed that, to find an optimal solution (with a given probability), the number of samples drawn in ransac is significantly higher than predicted from the mathematical model.
Ondrej Chum, Jiri Matas, Josef Kittler
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A new enhancement of ransac, the locally optimized ransac (lo-ransac), is introduced. It has been observed that, to find an optimal solution (with a given probability), the number of samples drawn in ransac is significantly higher than predicted from the mathematical model.
Ondrej Chum, Jiri Matas, Josef Kittler
openaire +1 more source