article

Image deformation using moving least squares

Authors:

Scott Schaefer,

Travis McPhail,

Joe WarrenAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 25, Issue 3

Pages 533 - 540

https://doi.org/10.1145/1141911.1141920

Published: 01 July 2006 Publication History

Abstract

We provide an image deformation method based on Moving Least Squares using various classes of linear functions including affine, similarity and rigid transformations. These deformations are realistic and give the user the impression of manipulating real-world objects. We also allow the user to specify the deformations using either sets of points or line segments, the later useful for controlling curves and profiles present in the image. For each of these techniques, we provide simple closed-form solutions that yield fast deformations, which can be performed in real-time.

Supplementary Material

JPG File (p533-schaefer-high.jpg)

Download
15.32 KB

JPG File (p533-schaefer-low.jpg)

Download
12.40 KB

High Resolution (p533-schaefer-high.mov)

Download
30.35 MB

Low Resolution (p533-schaefer-low.mov)

Download
11.44 MB

References

[1]

Alexa, M., Cohen-Or, D., and Levin, D. 2000. Asrigid-as-possible shape interpolation. In Proceedings of ACM SIGGRAPH 2000, ACM Press/Addison-Wesley Publishing Co., New York, NY, USA, 157--164.

Digital Library

[2]

Beier, T., and Neely, S. 1992. Feature-based image metamorphosis. In SIGGRAPH '92: Proceedings of the 19th annual conference on Computer graphics and interactive techniques, ACM Press, New York, NY, USA, 35--42.

Digital Library

[3]

Bookstein, F. L. 1989. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11, 6, 567--585.

Digital Library

[4]

Gu, X., and Yau, S.-T. 2003. Global conformal surface parameterization. In SGP '03: Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 127--137.

Digital Library

[5]

Horn, B. 1987. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America A 4, 4 (April), 629--642.

[6]

Igarashi, T., Moscovich, T., and Hughes, J. F. 2005. Asrigid-as-possible shape manipulation. ACM Trans. Graph. 24, 3, 1134--1141.

Digital Library

[7]

Ju, T., Warren, J., Eichele, G., Thaller, C., Chiu, W., and Carson, J. 2003. A geometric database for gene expression data. In SGP '03: Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing, 166--176.

Digital Library

[8]

Kobayashi, K. G., and Ootsubo, K. 2003. t-ffd: free-form deformation by using triangular mesh. In SM '03: Proceedings of the eighth ACM symposium on Solid modeling and applications, ACM Press, 226--234.

Digital Library

[9]

Lee, S.-Y., Chwa, K.-Y., and Shin, S. Y. 1995. Image metamorphosis using snakes and free-form deformations. In SIGGRAPH '95: Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, ACM Press, New York, NY, USA, 439--448.

Digital Library

[10]

Levin, D. 1998. The approximation power of moving least-squares. Mathematics of Computation 67, 224, 1517--1531.

Digital Library

[11]

Maccracken, R., and Joy, K. I. 1996. Free-form deformations with lattices of arbitrary topology. In Proceedings of ACM SIGGRAPH 1996, ACM Press, 181--188.

Digital Library

[12]

Sederberg, T. W., and Parry, S. R. 1986. Free-form deformation of solid geometric models. In Proceedings of ACM SIGGRAPH 1986, ACM Press, 151--160.

Digital Library

[13]

Shepard, D. 1968. A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM national conference, ACM Press, 517--524.

Digital Library

[14]

Smythe, D. 1990. A two-pass mesh warping algorithm for object transformation and image interpolation. Tech. Rep. 1030, ILM Computer Graphics Department, Lucasfilm, San Rafael, Calif.

[15]

Tiddeman, B., Duffy, N., and Rabey, G. 2001. A general method for overlap control in image warping. Computers and Graphics 25, 1, 59--66.

[16]

Wolberg, G. 1998. Image morphing: a survey. The Visual Computer 14, 8/9, 360--372.

Cited By

Piccinini FTazzari MTumedei MStellato MRemondini DGiampieri EMartinelli GCastellani GCarbonaro A(2024)Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy ImagesSensors10.3390/s2402045124:2(451)Online publication date: 11-Jan-2024
https://doi.org/10.3390/s24020451
Miura T(2024)Quantitative Estimation of Local Distortions in Historical Maps by Moving Least Squares TechniqueJournal of Information Processing10.2197/ipsjjip.32.112532(1125-1128)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.1125
Agnolucci LGalteri LBertini MBimbo A(2024)Perceptual Quality Improvement in Videoconferencing Using Keyframes-Based GANIEEE Transactions on Multimedia10.1109/TMM.2023.326488226(339-352)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3264882
Show More Cited By

Index Terms

Image deformation using moving least squares
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

Recommendations

Robust moving least-squares fitting with sharp features

We introduce a robust moving least-squares technique for reconstructing a piecewise smooth surface from a potentially noisy point cloud. We use techniques from robust statistics to guide the creation of the neighborhoods used by the moving least squares ...
Image deformation using moving least squares
SIGGRAPH '06: ACM SIGGRAPH 2006 Papers

We provide an image deformation method based on Moving Least Squares using various classes of linear functions including affine, similarity and rigid transformations. These deformations are realistic and give the user the impression of manipulating real-...
Parabolic-cylindrical moving least squares surfaces

Moving least squares (MLS) surface approximation is a popular tool for the processing and reconstruction of non-structured and noisy point clouds. This paper introduces a new variant improving the approximation quality when the underlying surface is ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 25, Issue 3

July 2006

742 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1141911

Issue’s Table of Contents

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2006

Published in TOG Volume 25, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

562
Total Citations
View Citations
4,442
Total Downloads

Downloads (Last 12 months)56
Downloads (Last 6 weeks)5

Reflects downloads up to 28 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Piccinini FTazzari MTumedei MStellato MRemondini DGiampieri EMartinelli GCastellani GCarbonaro A(2024)Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy ImagesSensors10.3390/s2402045124:2(451)Online publication date: 11-Jan-2024
https://doi.org/10.3390/s24020451
Miura T(2024)Quantitative Estimation of Local Distortions in Historical Maps by Moving Least Squares TechniqueJournal of Information Processing10.2197/ipsjjip.32.112532(1125-1128)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.1125
Agnolucci LGalteri LBertini MBimbo A(2024)Perceptual Quality Improvement in Videoconferencing Using Keyframes-Based GANIEEE Transactions on Multimedia10.1109/TMM.2023.326488226(339-352)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3264882
Gros RRodríguez-Núñez OFelger LMoriconi SMcKinley RPierangelo ANovikova TVassella ESchucht PHewer EMaragkou T(2024)Characterization of Polarimetric Properties in Various Brain Tumor Types Using Wide-Field Imaging Mueller PolarimetryIEEE Transactions on Medical Imaging10.1109/TMI.2024.341328843:12(4120-4132)Online publication date: Dec-2024
https://doi.org/10.1109/TMI.2024.3413288
Liu JLiu HFu HYe YChen KLu YMao JXu RSun M(2024)Edge-Guided Contrastive Adaptation Network for Arteriovenous Nicking Classification Using Synthetic DataIEEE Transactions on Medical Imaging10.1109/TMI.2023.333216843:3(1237-1246)Online publication date: Mar-2024
https://doi.org/10.1109/TMI.2023.3332168
Zheng YPang KDas AChang DSong YMa Z(2024)CreativeSeg: Semantic Segmentation of Creative SketchesIEEE Transactions on Image Processing10.1109/TIP.2024.337419633(2266-2278)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3374196
Hu HChong CLee YYau KHuang H(2024)Single Image Reflection Removal with Data Augmentation-Based Training Images2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE)10.1109/ICAACE61206.2024.10549473(592-595)Online publication date: 1-Mar-2024
https://doi.org/10.1109/ICAACE61206.2024.10549473
Islam TMiron ALiu XLi Y(2024)Deep Learning in Virtual Try-On: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2024.336861212(29475-29502)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368612
Claasen Pde Villiers J(2024)Video-based sequential Bayesian homography estimation for soccer field registrationExpert Systems with Applications10.1016/j.eswa.2024.124156252(124156)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.124156
Straub ASadlo FErtl T(2024)Feature-based deformation for flow visualizationJournal of Visualization10.1007/s12650-024-00963-527:5(795-817)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s12650-024-00963-5
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents