Visual reconstruction
Robust regression and outlier detection
Robust regression and outlier detection
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing and computer vision: an introduction to theory and implementations
Digital image processing and computer vision: an introduction to theory and implementations
Robust regression methods for computer vision: a review
International Journal of Computer Vision
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in Curvature-Based Segmentation of Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Adaptive Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Computer and Robot Vision
Digital Image Processing
Computer Vision
Edge-Region-Based Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Segmentation of Range Images
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
Prediction Intervals for Surface Growing Range Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Segmentation of range images into planar regions
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
A survey of methods for recovering quadrics in triangle meshes
ACM Computing Surveys (CSUR)
Gradient-based polyhedral segmentation for range images
Pattern Recognition Letters
A Generic Methodology for Partitioning Unorganised 3D Point Clouds for Robotic Vision
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Range image segmentation based on randomized Hough transform
Pattern Recognition Letters
Automatic segmentation of unorganized noisy point clouds based on the Gaussian map
Computer-Aided Design
Surface classification from airborne laser scanning data
Computers & Geosciences
Segmentation of architecture shape information from 3D point cloud
Proceedings of the 8th International Conference on Virtual Reality Continuum and its Applications in Industry
CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data
Pattern Recognition Letters
Efficient robust digital hyperplane fitting with bounded error
DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
A method for footprint range image segmentation and description
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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This paper describes an unsupervised region merging technique based on a novel robust statistical test. The merging decision is derived from the mutual inlier ratio (MIR) of adjacent regions. This ratio is computed using robust regression techniques and a novel method to estimate the robust scale of the Gaussian distribution. A discrimination value to recognize identical Gaussian distributions with the MIR is derived theoretically as a function of the sizes of the compared sets. The presented method to test distributions is compared with the established Kolmogorov-Smirnov test and implemented into a segmentation algorithm for planar range images. The iterative region growing technique is evaluated using an established framework for range image segmentation comparison involving 60 real range images. The evaluation incorporates a comparison with four state-of-the-art algorithms and gives an experimental demonstration of the need for robust methods capable of handling noisy data in real applications.