Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Adaptive Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Robust Estimation Using Layers of Support
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
MIR: An Approach to Robust Clustering-Application to Range Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implementing a Multi-Model Estimation Method
International Journal of Computer Vision
Journal of Intelligent and Robotic Systems
Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting
International Journal of Computer Vision
Randomized Algorithms: A System-Level, Poly-Time Analysis of Robust Computation
IEEE Transactions on Computers
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Active Shape Model Search
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation
International Journal of Computer Vision
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Multivariate Analysis
Consistent parameter clustering: Definition and analysis
Pattern Recognition Letters
Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
Improving the quality of color colonoscopy videos
Journal on Image and Video Processing - Color in Image and Video Processing
Robust Multiple Structures Estimation with J-Linkage
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Journal of Mathematical Imaging and Vision
Range segmentation of large building exteriors: A hierarchical robust approach
Computer Vision and Image Understanding
CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data
Pattern Recognition Letters
An M-estimator for high breakdown robust estimation in computer vision
Computer Vision and Image Understanding
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
Finite sample bias of robust scale estimators in computer vision problems
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Generalised relaxed Radon transform (GR2T) for robust inference
Pattern Recognition
Editor's Choice Article: Image-consistent patches from unstructured points with J-linkage
Image and Vision Computing
A simultaneous sample-and-filter strategy for robust multi-structure model fitting
Computer Vision and Image Understanding
Hi-index | 0.15 |
When fitting models to data containing multiple structures, such as when fitting surface patches to data taken from a neighborhood that includes a range discontinuity, robust estimators must tolerate both gross outliers and pseudo outliers. Pseudo outliers are outliers to the structure of interest, but inliers to a different structure. They differ from gross outliers because of their coherence. Such data occurs frequently in computer vision problems, including motion estimation, model fitting, and range data analysis. The focus in this paper is the problem of fitting surfaces near discontinuities in range data.To characterize the performance of least median of the squares, least trimmed squares, M-estimators, Hough transforms, RANSAC, and MINPRAN on this type of data, the "pseudo outlier bias" metric is developed using techniques from the robust statistics literature, and it is used to study the error in robust fits caused by distributions modeling various types of discontinuities. The results show each robust estimator to be biased at small, but substantial, discontinuities. They also show the circumstances under which different estimators are most effective. Most importantly, the results imply present estimators should be used with care, and new estimators should be developed.