Bilateral functions for global motion modeling

Wen Yan Daniel Lin, Ming Ming Cheng, Jiangbo Lu, Hongsheng Yang, Minh N Do, Philip Torr

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

Abstract

This paper proposes modeling motion in a bilateral domain that augments spatial information with the motion itself. We use the bilateral domain to reformulate a piecewise smooth constraint as continuous global modeling constraint. The resultant model can be robustly computed from highly noisy scattered feature points using a global minimization. We demonstrate how the model can reliably obtain large numbers of good quality correspondences over wide baselines, while keeping outliers to a minimum.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
PublisherSpringer-Verlag
Pages341-356
Number of pages16
EditionPART 4
ISBN (Print)9783319105925
DOIs
StatePublished - Jan 1 2014
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: Sep 6 2014Sep 12 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume8692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th European Conference on Computer Vision, ECCV 2014
CountrySwitzerland
CityZurich
Period9/6/149/12/14

Fingerprint

Global Minimization
Motion
Feature Point
Spatial Information
Modeling
Outlier
Baseline
Correspondence
Model
Demonstrate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lin, W. Y. D., Cheng, M. M., Lu, J., Yang, H., Do, M. N., & Torr, P. (2014). Bilateral functions for global motion modeling. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings (PART 4 ed., pp. 341-356). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8692 LNCS, No. PART 4). Springer-Verlag. https://doi.org/10.1007/978-3-319-10593-2_23

Bilateral functions for global motion modeling. / Lin, Wen Yan Daniel; Cheng, Ming Ming; Lu, Jiangbo; Yang, Hongsheng; Do, Minh N; Torr, Philip.

Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 4. ed. Springer-Verlag, 2014. p. 341-356 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8692 LNCS, No. PART 4).

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

Lin, WYD, Cheng, MM, Lu, J, Yang, H, Do, MN & Torr, P 2014, Bilateral functions for global motion modeling. in Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 4 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 4, vol. 8692 LNCS, Springer-Verlag, pp. 341-356, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 9/6/14. https://doi.org/10.1007/978-3-319-10593-2_23
Lin WYD, Cheng MM, Lu J, Yang H, Do MN, Torr P. Bilateral functions for global motion modeling. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 4 ed. Springer-Verlag. 2014. p. 341-356. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 4). https://doi.org/10.1007/978-3-319-10593-2_23
Lin, Wen Yan Daniel ; Cheng, Ming Ming ; Lu, Jiangbo ; Yang, Hongsheng ; Do, Minh N ; Torr, Philip. / Bilateral functions for global motion modeling. Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 4. ed. Springer-Verlag, 2014. pp. 341-356 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 4).
@inproceedings{be0bb8836ceb43bba8d80d36bbf96189,
title = "Bilateral functions for global motion modeling",
abstract = "This paper proposes modeling motion in a bilateral domain that augments spatial information with the motion itself. We use the bilateral domain to reformulate a piecewise smooth constraint as continuous global modeling constraint. The resultant model can be robustly computed from highly noisy scattered feature points using a global minimization. We demonstrate how the model can reliably obtain large numbers of good quality correspondences over wide baselines, while keeping outliers to a minimum.",
author = "Lin, {Wen Yan Daniel} and Cheng, {Ming Ming} and Jiangbo Lu and Hongsheng Yang and Do, {Minh N} and Philip Torr",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-10593-2_23",
language = "English (US)",
isbn = "9783319105925",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
number = "PART 4",
pages = "341--356",
booktitle = "Computer Vision, ECCV 2014 - 13th European Conference, Proceedings",
edition = "PART 4",

}

TY - GEN

T1 - Bilateral functions for global motion modeling

AU - Lin, Wen Yan Daniel

AU - Cheng, Ming Ming

AU - Lu, Jiangbo

AU - Yang, Hongsheng

AU - Do, Minh N

AU - Torr, Philip

PY - 2014/1/1

Y1 - 2014/1/1

N2 - This paper proposes modeling motion in a bilateral domain that augments spatial information with the motion itself. We use the bilateral domain to reformulate a piecewise smooth constraint as continuous global modeling constraint. The resultant model can be robustly computed from highly noisy scattered feature points using a global minimization. We demonstrate how the model can reliably obtain large numbers of good quality correspondences over wide baselines, while keeping outliers to a minimum.

AB - This paper proposes modeling motion in a bilateral domain that augments spatial information with the motion itself. We use the bilateral domain to reformulate a piecewise smooth constraint as continuous global modeling constraint. The resultant model can be robustly computed from highly noisy scattered feature points using a global minimization. We demonstrate how the model can reliably obtain large numbers of good quality correspondences over wide baselines, while keeping outliers to a minimum.

UR - http://www.scopus.com/inward/record.url?scp=84906506066&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84906506066&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-10593-2_23

DO - 10.1007/978-3-319-10593-2_23

M3 - Conference contribution

SN - 9783319105925

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

SP - 341

EP - 356

BT - Computer Vision, ECCV 2014 - 13th European Conference, Proceedings

PB - Springer-Verlag

ER -