by Noakes, L, Kozera, R and Klette, R
Abstract:
In this paper we analyze a specific problem within the context of recovering the geometric shape of an unknown surface from multiple noisy shading patterns generated by consecutive parallel illuminations by different light-sources. Shading-based single-view shape recovery in computer vision often leads to vector fields (i.e. estimated surface normals) which have to be integrated for calculations of height or depth maps. We present an algorithm for enforcing the integrability condition of a given non-integrable vector field which ensures a global suboptimal solution by local optimizations. The scheme in question relies neither on a priori knowledge of boundary conditions nor on other global constraints imposed on the so-far derived noise contaminated gradient integration techniques. The discussion is supplemented by examples illustrating algorithm performance.
Reference:
Lawn-mowing algorithm for noisy gradient vector fields (Noakes, L, Kozera, R and Klette, R), In Proceedings of SPIE – The International Society for Optical Engineering, volume 3811, 1999.
Bibtex Entry:
@inproceedings{noakes1999lawn-mowingfields,
author = "Noakes, L and Kozera, R and Klette, R",
booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",
pages = "305--316",
title = "Lawn-mowing algorithm for noisy gradient vector fields",
volume = "3811",
year = "1999",
abstract = "In this paper we analyze a specific problem within the context of recovering the geometric shape of an unknown surface from multiple noisy shading patterns generated by consecutive parallel illuminations by different light-sources. Shading-based single-view shape recovery in computer vision often leads to vector fields (i.e. estimated surface normals) which have to be integrated for calculations of height or depth maps. We present an algorithm for enforcing the integrability condition of a given non-integrable vector field which ensures a global suboptimal solution by local optimizations. The scheme in question relies neither on a priori knowledge of boundary conditions nor on other global constraints imposed on the so-far derived noise contaminated gradient integration techniques. The discussion is supplemented by examples illustrating algorithm performance.",
issn = "0277-786X",
language = "eng",
}