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Euclidean and affine curve reconstruction

Authors: Jose Agudelo; Brooke Dippold; Ian Klein; Alex Kokot; Eric Geiger; Irina A. Kogan;

Euclidean and affine curve reconstruction

Abstract

We consider practical aspects of reconstructing planar curves with prescribed Euclidean or affine curvatures. These curvatures are invariant under the special Euclidean group and the equi-affine groups, respectively, and play an important role in computer vision and shape analysis. We discuss and implement algorithms for such reconstruction, and give estimates on how close reconstructed curves are relative to the closeness of their curvatures in appropriate metrics. Several illustrative examples are provided.

This paper is a result of an REU project conducted at the North Carolina State University in the Summer and Fall 2020. This version has several minor corrections

Keywords

Mathematics - Differential Geometry, FOS: Computer and information sciences, Geometric methods in ordinary differential equations, Curves in Euclidean and related spaces, Hausdorff distance, 53A04, 53A15, 53A55, 34A45, 68T45, Computer Vision and Pattern Recognition (cs.CV), Affine differential geometry, Computer Science - Computer Vision and Pattern Recognition, Differential invariants (local theory), geometric objects, planar curves, Machine vision and scene understanding, affine transformations, Computer-aided design (modeling of curves and surfaces), Differential Geometry (math.DG), curve reconstruction, affine curvature, Numerical smoothing, curve fitting, FOS: Mathematics, Theoretical approximation of solutions to ordinary differential equations

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green