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Jarvis73
177 Stars 47 Forks MIT License 12 Commits 2 Opened issues

Description

Implementation of three algorithms of image deformation using moving least squares. http://dl.acm.org/citation.cfm?doid=1179352.1141920

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Moving Least Squares (MLS)

Update: 2020-09-25 No need for so-called inverse transformation. Just transform target pixels to the corresponding source pixels.

Introduction

Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.

In computer graphics, the moving least squares method is useful for reconstructing a surface from a set of points. Often it is used to create a 3D surface from a point cloud through either downsampling or upsampling.

Methods

  • Affine deformation
  • Similarity deformation
  • Rigid deformation

Preview

  • Toy

Affine deformation

Similarity deformation

Rigid deformation

  • Monalisa

Rigid deformation

  • Cells

Rigid Deformation

Code list

  • img_utils.py
    : Implementation of the algorithms
  • img_utils_demo.py
    : Demo program
  • read_tif.py
    : TIF file reader
  • tiff_deformation.py
    : Demo program

Reference

[1] Schaefer S, Mcphail T, Warren J. Image deformation using moving least squares[C]// ACM SIGGRAPH. ACM, 2006:533-540.

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