A New Method of Ski Tracks Extraction Based on Laser Intensity Information by Wenxin Wang, Changming Zhao, Haiyang Zhang

“…By comparing the proposed method to the Euclidean distance method, the clustering segmentation method, and the RANSAC method, the average extraction accuracy is increased by 16.9%, while the over extraction rate is reduced by 8.4% and the under extraction rate is reduced by 8.6%, allowing us to accurately extract the ski track point cloud of a ski resort.…”
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IMAGE-BASED CONTROL POINT DETECTION AND CORRESPONDENCE-FREE GEOREFERENCING by C. Benz, V. Rodehorst

“…For associating detected and geodetically measured points, a RANSAC-based procedure is presented that determines a geometrically consistent transformation between detected and measured points.…”
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Does the 3D Feature Descriptor Impact The Registration Accuracy in Laparoscopic Liver Surgery? by Krames Lorena, Suppa Per, Nahm Werner

“…Registration was performed using the RANSAC algorithm. FPFH outperformed TOLDI for small surface patches and in case of Gaussian deformations in terms of registration accuracy. …”
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A new range‐only measurement‐based glass line feature extraction method by Seung Hwan Lee, Jung Hyun Oh, Ye Chan An

“…The extracted features are accumulated and reformulated for the generation of glass line features using the RANdom SAmple Consensus algorithm (RANSAC). Two experiments with different robot paths were conducted in a glass environment. …”
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Coarse-Fine-Stitched: A Robust Maritime Horizon Line Detection Method for Unmanned Surface Vehicle Applications by Yuan Sun, Li Fu

“…Finally, the fine line segments are stitched to obtain the whole horizon line based on random sample consensus (RANSAC). Using real data in the maritime environment, the experimental results demonstrate the effectiveness of CFS, compared to the existing methods in terms of accuracy and robustness.…”
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Location recognition in laparoscopic surgery by Gaubatz Jakob, Hartwig Regine, Wilhelm Dirk

“…A two-staged consistency check using a random sample consensus (RANSAC) and the data measured by the IMU ensure a high matching precision.…”
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The Integration of the Image Sensor with a 3-DOF Pneumatic Parallel Manipulator by Hao-Ting Lin, Mao-Hsiung Chiang

“…In order to accurately mark the center of target and strengthen the feature matching results, the random sample and consensus method (RANSAC) is utilized. The ASUS Xtion Pro Live depth camera which can directly estimate the 3-D location of the target point is used in this study. …”
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Fusion of semantic and appearance features for loop‐closure detection in a dynamic environment by Yan Xu, Jiani Huang

“…The loop‐closure matches are verified based on LDB descriptors and random sample consensus (RANSAC). Experimental results show that the proposed method can obtain a higher recall rate under 100% precision with less execution time per frame on several public datasets compared with other typical or state‐of‐the‐art algorithms.…”
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A New Machine Vision Method for Target Detection and Localization of Malleable Iron Pipes: An Experimental Case by Zhongqiang Pan, Dong Zhang

“…Point cloud images of malleable iron pipes are obtained by the Random Sample Consensus (RANSAC) algorithm, and precise matching is completed by the Iterative Closest Point (ICP) algorithm to obtain more accurate positions, so as to realize robot grasping. …”
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Edge-Triggered Three-Dimensional Object Detection Using a LiDAR Ring by Eunji Song, Seyoung Jeong, Sung-Ho Hwang

“…Verification of the results of removing the ground and extracting points through Ring Edge was conducted using SemanticKITTI and Waymo Open Dataset, and it was confirmed that both F1 scores were superior to RANSAC. In addition, extracting bounding boxes of objects also showed higher PDR index performance when verified in open datasets, virtual driving environments, and actual driving environments.…”
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Comprehensive approach for building outline extraction from LiDAR data with accent to a sparse laser scanning point cloud by Petr Hofman, Markéta Potůčková

“…Next, local planes are fitted to each point using RANSAC and further refined by least squares adjustment. …”
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Study on Elimination Algorithms for Line Segment Mismatches by Chang Li, Wenqi Jia, Dong Wei

“…Therefore, this work systematically studies elimination algorithms of line segment mismatches by combining 2 transformation models (i.e., affine and homography) with 2 M-estimators or 2 sample consensus methods (i.e., random sample consensus, RANSAC, and least median of squares, LMedS). The main idea is as follows. …”
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Keypoint-based passive method for image manipulation detection by Choudhary Shyam Prakash, Hari Om, Sushila Maheshkar, Vikas Maheshkar

“…For each extracted interest points we calculate the descriptors using SIFT then for the matching process of obtained descriptors, we use the outlier rejection with the nearest neighbour. Here, RANSAC is used to find the best set of matches to identify the manipulated regions. …”
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Analysis Of Sift And Surf Algorithms For Image Mosaicing On Embedded Platform by Ooi , Chong Wei

“…Next homography estimation is performed by using RANSAC algorithm. Perspective transform is applied to obtain a transformation for mapping a two dimensional quadrilateral into another. …”
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Accuracy assessment of the effect of different feature descriptors on the automatic co-registration of overlapping images by Oluibukun Gbenga Ajayi, Ifeanyi Jonathan Nwadialor

“…Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). …”
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Building Keypoint Mappings on Multispectral Images by a Cascade of Classifiers with a Resurrection Mechanism by Yong Li, Jing Jing, Hongbin Jin, Wei Qiao

“…The cascade structure is composed of four steps by utilizing best bin first (BBF), color and intensity distribution of segment (CIDS), global information and the RANSAC process to remove outlier keypoint matchings. …”
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SEMI-AUTOMATIC IMAGE-BASED CO-REGISTRATION OF RANGE IMAGING DATA WITH DIFFERENT CHARACTERISTICS by M. Weinmann, S. Wursthorn, B. Jutzi

“…In this paper, an automatic image-based coregistration methodology is presented which uses a RANSAC-based scheme for the Efficient Perspective-n-Point (EPnP) algorithm. …”
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Research on Multi-View 3D Reconstruction Technology Based on SFM by Lei Gao, Yingbao Zhao, Jingchang Han, Huixian Liu

“…This paper designs and implements a set of multi-view 3D reconstruction technology, adopts the fusion method of SIFT and SURF feature-point extraction results, increases the number of feature points, adds proportional constraints to improve the robustness of feature-point matching, and uses RANSAC to eliminate false matching. In the sparse reconstruction stage, the traditional incremental SFM algorithm takes a long time, but the accuracy is high; the traditional global SFM algorithm is fast, but its accuracy is low; aiming at the disadvantages of traditional SFM algorithm, this paper proposes a hybrid SFM algorithm, which avoids the problem of the long time consumption of incremental SFM and the problem of the low precision and poor robustness of global SFM; finally, the MVS algorithm of depth-map fusion is used to complete the dense reconstruction of objects, and the related algorithms are used to complete the surface reconstruction, which makes the reconstruction model more realistic.…”
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Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection by Rui Gao, Changyun Miao, Xianguo Li

“…Thirdly, only for the IOI, the feature-based partition and block registration method is used to register the images more accurately, the overlapping region is adaptively segmented, the speeded up robust features (SURF) algorithm is used to extract the feature points, and the random sample consensus (RANSAC) algorithm is used to achieve accurate registration. …”
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ROOF RECONSTRUCTION FROM AIRBORNE LASER SCANNING DATA BASED ON IMAGE PROCESSING METHODS by S. Goebbels, R. Pohle-Fröhlich

“…The approach is based on image processing methods applied to an interpolated height map and avoids shortcomings of established methods for plane detection like Hough transform or RANSAC algorithms on point clouds. The improvement originates in an interpolation algorithm that generates a height map from sparse point cloud data by preserving ridge lines and step edges of roofs. …”
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