A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
Guided Sampling and Consensus for Motion Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation
International Journal of Computer Vision
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-View Multibody Structure-and-Motion with Outliers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Robust Multiple Structures Estimation with J-Linkage
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Automatic Estimation of the Inlier Threshold in Robust Multiple Structures Fitting
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Robust and efficient feature tracking for indoor navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information fusion for multi-camera and multi-body structure and motion
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Combining plane estimation with shape detection for holistic scene understanding
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Editor's Choice Article: Image-consistent patches from unstructured points with J-linkage
Image and Vision Computing
Demisting the Hough Transform for 3D Shape Recognition and Registration
International Journal of Computer Vision
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Common problem encountered in the analysis of dynamic scene is the problem of simultaneous estimation of the number of models and their parameters. This problem becomes difficult as the measurement noise in the data increases and the data are further corrupted by outliers. This is especially the case in a variety of motion estimation problems, where the displacement between the views is large and the process of establishing correspondences is difficult. In this paper we propose a novel nonparametric sampling based method for estimating the number of models and their parameters. The main novelty of the proposed method lies in the analysis of the distribution of residuals of individual data points with respect to the set of hypotheses, generated by a RANSAC-like sampling process. We will show that the modes of the residual distributions directly reveal the presence of multiple models and facilitate the recovery of the individual models, without making any assumptions about the distribution of the outliers or the noise process. The proposed approach is capable of handling data with a large fraction of outliers. Experiments with both synthetic data and image pairs related by different motion models are presented to demonstrate the effectiveness of the proposed approach.