Invariant surface characteristics for 3D object recognition in range images
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
Computer vision theory: The lack thereof
Computer Vision, Graphics, and Image Processing
Empirical model-building and response surface
Empirical model-building and response surface
Three-dimensional computer vision
Three-dimensional computer vision
Using Polygons to Recognize and Locate Partially Occluded Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and Classification of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression and outlier detection
Robust regression and outlier detection
Computer Vision, Graphics, and Image Processing
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
From surfaces to objects: computer vision and three dimensional scene analysis
From surfaces to objects: computer vision and three dimensional scene analysis
Robust regression methods for computer vision: a review
International Journal of Computer Vision
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comments on "Nonparametric Segmentation of Curves Into Various Representations"
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Adaptive Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation of Rotation Angles from Image Sequences Usingthe Annealing M-Estimator
Journal of Mathematical Imaging and Vision
Geometric Information Criterion for Model Selection
International Journal of Computer Vision
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency sequential surface organization for free-form object recognition
Computer Vision and Image Understanding
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A sequential algorithm for motion estimation from point correspondences with intermittent occlusions
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Statistically-efficient filtering in impulsive environments: weighted myriad filters
EURASIP Journal on Applied Signal Processing
A comparative study of model selection criteria for computer vision applications
Image and Vision Computing
A geometric approach to the segmentation of range images
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data
Pattern Recognition Letters
Fast polynomial segmentation of digitized curves
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
| Hi-index | 0.14 |
Presents an autonomous, statistically robust, sequential function approximation approach to simultaneous parameterization and organization of (possibly partially occluded) surfaces in noisy, outlier-ridden (not Gaussian), functional range data. At the core of this approach is the Robust Sequential Estimator, a robust extension to the method of sequential least squares. Unlike most existing surface characterization techniques, the authors' method generates complete surface hypotheses in parameter space. Given a noisy depth map of an unknown 3-D scene, the algorithm first selects appropriate seed points representing possible surfaces. For each nonredundant seed it chooses the best approximating model from a given set of competing models using a modified Akaike Information Criterion. With this best model, each surface is expanded from its seed over the entire image, and this step is repeated for all seeds. Those points which appear to be outliers with respect to the model in growth are not included in the (possibly disconnected) surface. Point regions are deleted from each newly grown surface in the prune stage. Noise, outliers, or coincidental surface alignment may cause some points to appear to belong to more than one surface. These ambiguities are resolved by a weighted voting scheme within a 5/spl times/5 decision window centered around the ambiguous point. The isolated point regions left after the resolve stage are removed and any missing points in the data are filled by the surface having a majority consensus in an 8-neighborhood.