3-D motion estimation for positioning from 2-D acoustic video imagery

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

11 Citations (Scopus)

Abstract

We address the problem of estimating 3-D motion from acoustic images acquired by high-frequency 2-D imaging sonars deployed in underwater. Utilizing a planar approximation to scene surfaces, two-view homography is the basis of a nonlinear optimization method for estimating the motion parameters. There is no scale factor ambiguity, unlike the case of monocular motion vision for optical images. Experiments with real images demonstrate the potential in a range of applications, including target-based positioning in search and inspection operations.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages80-88
Number of pages9
Volume4478 LNCS
EditionPART 2
StatePublished - Dec 1 2007
Event3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007 - Girona, Spain
Duration: Jun 6 2007Jun 8 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4478 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007
CountrySpain
CityGirona
Period6/6/076/8/07

Fingerprint

Imagery (Psychotherapy)
Motion Estimation
Motion estimation
Acoustics
Positioning
3D
Inspection
Imaging techniques
Motion
Monocular Vision
Homography
Scale factor
Experiments
Nonlinear Optimization
Optimization Methods
Imaging
Target
Approximation
Range of data
Demonstrate

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Sekkati, H., & Negahdaripour, S. (2007). 3-D motion estimation for positioning from 2-D acoustic video imagery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 4478 LNCS, pp. 80-88). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4478 LNCS, No. PART 2).

3-D motion estimation for positioning from 2-D acoustic video imagery. / Sekkati, H.; Negahdaripour, Shahriar.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4478 LNCS PART 2. ed. 2007. p. 80-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4478 LNCS, No. PART 2).

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

Sekkati, H & Negahdaripour, S 2007, 3-D motion estimation for positioning from 2-D acoustic video imagery. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 4478 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 4478 LNCS, pp. 80-88, 3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007, Girona, Spain, 6/6/07.
Sekkati H, Negahdaripour S. 3-D motion estimation for positioning from 2-D acoustic video imagery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 4478 LNCS. 2007. p. 80-88. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
Sekkati, H. ; Negahdaripour, Shahriar. / 3-D motion estimation for positioning from 2-D acoustic video imagery. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4478 LNCS PART 2. ed. 2007. pp. 80-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
@inproceedings{26b3cc684a754f008cd800dbba1d3abb,
title = "3-D motion estimation for positioning from 2-D acoustic video imagery",
abstract = "We address the problem of estimating 3-D motion from acoustic images acquired by high-frequency 2-D imaging sonars deployed in underwater. Utilizing a planar approximation to scene surfaces, two-view homography is the basis of a nonlinear optimization method for estimating the motion parameters. There is no scale factor ambiguity, unlike the case of monocular motion vision for optical images. Experiments with real images demonstrate the potential in a range of applications, including target-based positioning in search and inspection operations.",
author = "H. Sekkati and Shahriar Negahdaripour",
year = "2007",
month = "12",
day = "1",
language = "English",
isbn = "9783540728481",
volume = "4478 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "80--88",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 2",

}

TY - GEN

T1 - 3-D motion estimation for positioning from 2-D acoustic video imagery

AU - Sekkati, H.

AU - Negahdaripour, Shahriar

PY - 2007/12/1

Y1 - 2007/12/1

N2 - We address the problem of estimating 3-D motion from acoustic images acquired by high-frequency 2-D imaging sonars deployed in underwater. Utilizing a planar approximation to scene surfaces, two-view homography is the basis of a nonlinear optimization method for estimating the motion parameters. There is no scale factor ambiguity, unlike the case of monocular motion vision for optical images. Experiments with real images demonstrate the potential in a range of applications, including target-based positioning in search and inspection operations.

AB - We address the problem of estimating 3-D motion from acoustic images acquired by high-frequency 2-D imaging sonars deployed in underwater. Utilizing a planar approximation to scene surfaces, two-view homography is the basis of a nonlinear optimization method for estimating the motion parameters. There is no scale factor ambiguity, unlike the case of monocular motion vision for optical images. Experiments with real images demonstrate the potential in a range of applications, including target-based positioning in search and inspection operations.

UR - http://www.scopus.com/inward/record.url?scp=38049153594&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38049153594&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9783540728481

VL - 4478 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 80

EP - 88

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -