Estimating the orientation of planar surfaces using the phase differencing algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a parametric solution to the problem of estimating the orientation in space of a planar textured surface, from a single observed image of it. The coordinate transformation from surface to image coordinates, due to the perspective projection, transforms each homogeneous sinusoidal component of the surface texture into a sinusoid whose frequency is a function of location. It is shown in this paper that the phase of each of the sinusoids can be expressed as a linear function of some constants that are related, in a rather simple form, to the surface tilt and slant angles. Using the phase differencing algorithm we fit a polynomial phase model to a sinusoidal component of the observed texture. Substituting in the derived linear relation, the unknown phase with the one estimated using the phase differencing algorithm, we obtain a closed form, analytic, and computationally efficient solution to the problem of estimating the tilt and slant angles. The algorithm is shown to produce estimates that are close to the Cramer-Rao bound, at computational complexity which is considerably lower than that of any existing algorithm.

Original languageEnglish
Title of host publicationProceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999
PublisherInstitute of Electrical and Electronics Engineers
Pages178-182
Number of pages5
ISBN (Electronic)0769501400, 9780769501406
DOIs
StatePublished - 1 Jan 1999
Event1999 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999 - Caesarea, Israel
Duration: 14 Jun 199916 Jun 1999

Publication series

NameProceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999

Conference

Conference1999 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999
Country/TerritoryIsrael
CityCaesarea
Period14/06/9916/06/99

ASJC Scopus subject areas

  • Signal Processing
  • Statistics and Probability

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