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.

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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

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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]

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Association for Computing Machinery

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Publication History

Published: 01 July 2006

Published in TOG Volume 25, Issue 3

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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
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
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
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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
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Tang RWang WHan YFeng X(2024)Grownbb: Gromov–Wasserstein learning of neural best buddies for cross-domain correspondenceThe Visual Computer10.1007/s00371-023-03251-940:12(8517-8530)Online publication date: 12-Feb-2024
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