Range image reconstruction based on robust multiresolution estimation of surface parameters

Authors:
In Su Chang;Rae-Hong Park
Affiliations:
Department of Electronic Engineering, Sogang University, C.P.O. Box 1142, Seoul 100-611, South Korea;Department of Electronic Engineering, Sogang University, C.P.O. Box 1142, Seoul 100-611, South Korea
Venue:
Pattern Recognition Letters
Year:
2003

Citing 10
Cited 0

Robust regression and outlier detection

Robust regression and outlier detection
Adaptive pattern recognition and neural networks

Adaptive pattern recognition and neural networks
Robust regression methods for computer vision: a review

International Journal of Computer Vision
Estimation of Planar Curves, Surfaces, and Nonplanar Space Curves Defined by Implicit Equations with Applications to Edge and Range Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward 3D vision from range images: an optimization framework and parallel networks

CVGIP: Image Understanding
Multiresolution adaptive image smoothing

CVGIP: Graphical Models and Image Processing
An Experimental Comparison of Range Image Segmentation Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Adaptive Segmentation of Range Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of 3-D Closed Surfaces Using Progressive Shell Models

ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
A robust nonlinear filter for image restoration

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.10

Visualization

Abstract

This paper proposes two robust multiresolution estimation methods of surface parameters for range images. Based on the robust estimation of surface parameters, the proposed methods approximate a patch to a planar surface in the locally adaptive window. Selection of resolution is made pixelwise by comparing a locally computed homogeneity measure with the global threshold obtained by the distribution of the approximation error. The proposed multiresolution surface parameter estimation methods are applied to range image reconstruction and segmentation. Computer simulation results with noisy images contaminated by additive Gaussian noise and impulse noise show that the proposed multiresolution reconstruction methods preserve step and roof edges better than the conventional methods. Also the segmentation methods based on the estimated surface parameters are shown to be robust to noise.