Opti-acoustic stereo imaging, system calibration and 3-D reconstruction

S. Negahdaripour, H. Sekkati, H. Pirsiavash

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Utilization of an acoustic camera for range measurements is a key advantage for 3-D shape recovery of underwater targets by opti-acoustic stereo imaging, where the associated epipolar geometry of optical and acoustic image correspondences can be described in terms of conic sections. In this paper, we propose methods for system calibration and 3-D scene reconstruction by maximum likelihood estimation from noisy image measurements. The recursive 3-D reconstruction method utilized as initial condition a closed-form solution that integrates the advantages of so-called range and azimuth solutions. Synthetic data tests are given to provide insight into the merits of the new target imaging and 3-D reconstruction paradigm, while experiments with real data confirm the findings based on computer simulations, and demonstrate the merits of this novel 3-D reconstruction paradigm.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - Oct 11 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

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ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Negahdaripour, S., Sekkati, H., & Pirsiavash, H. (2007). Opti-acoustic stereo imaging, system calibration and 3-D reconstruction. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270359] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383361