by Wei, T and Klette, R
Abstract:
This paper presents a class of Fourier transform based approaches for Height from Gradients (HFG) problem, which is to reconstruct the 3D surface height of an object from its gradients. The HFG problem results from quite a few research areas in computer vision such as shape from shading, photometric stereo, shape from texture, shape from contours and so on. The proposed methods have some distinct advantages. Firstly, the derivation process of the algorithms has generality, and can be used for more functionals dealing with additional constraints. Secondly, they are noniterative so that boundary conditions are not needed. In addition, their robustness to noisy gradient estimates can be improved by choosing associated weighting parameters. Experimental results using synthetic and real images are also presented. © 2004 IEEE.
Reference:
Fourier transform based methods for height from gradients (Wei, T and Klette, R), In 2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV), volume 1, 2004.
Bibtex Entry:
@inproceedings{wei2004fouriergradients,
author = "Wei, T and Klette, R",
booktitle = "2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV)",
pages = "85--91",
title = "Fourier transform based methods for height from gradients",
volume = "1",
year = "2004",
abstract = "This paper presents a class of Fourier transform based approaches for Height from Gradients (HFG) problem, which is to reconstruct the 3D surface height of an object from its gradients. The HFG problem results from quite a few research areas in computer vision such as shape from shading, photometric stereo, shape from texture, shape from contours and so on. The proposed methods have some distinct advantages. Firstly, the derivation process of the algorithms has generality, and can be used for more functionals dealing with additional constraints. Secondly, they are noniterative so that boundary conditions are not needed. In addition, their robustness to noisy gradient estimates can be improved by choosing associated weighting parameters. Experimental results using synthetic and real images are also presented. © 2004 IEEE.",
isbn = "0780386531",
language = "eng",
}