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Area and Length Preserving Geometric Invariant Scale-Spaces

Publication ,  Journal Article
Tannenbaum, A; Sapiro, G
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence
January 1, 1995

In this paper, area preserving multi-scale representations of planar curves are described. This allows smoothing without shrinkage at the same time preserving all the scale-space properties. The representations are obtained deforming the curve via geometric heat flows while simultaneously magnifying the plane by a homethety which keeps the enclosed area constant When the Euclidean geometric heat flow is used, the resulting representation is Euclidean invariant, and similarly it is affine invariant when the affine one is used. The flows are geometrically intrinsic to the curve, and exactly satisfy all the basic requirements of scale-space representations. In the case of the Euclidean heat flow, it is completely local as well. The same approach is used to define length preserving geometric flows. A similarity (scale) invariant geometric heat flow is studied as well in this work. © 1995 IEEE

Duke Scholars

Published In

IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI

ISSN

0162-8828

Publication Date

January 1, 1995

Volume

17

Issue

1

Start / End Page

67 / 72

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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ICMJE
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Tannenbaum, A., & Sapiro, G. (1995). Area and Length Preserving Geometric Invariant Scale-Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), 67–72. https://doi.org/10.1109/34.368150
Tannenbaum, A., and G. Sapiro. “Area and Length Preserving Geometric Invariant Scale-Spaces.” IEEE Transactions on Pattern Analysis and Machine Intelligence 17, no. 1 (January 1, 1995): 67–72. https://doi.org/10.1109/34.368150.
Tannenbaum A, Sapiro G. Area and Length Preserving Geometric Invariant Scale-Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1995 Jan 1;17(1):67–72.
Tannenbaum, A., and G. Sapiro. “Area and Length Preserving Geometric Invariant Scale-Spaces.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 1, Jan. 1995, pp. 67–72. Scopus, doi:10.1109/34.368150.
Tannenbaum A, Sapiro G. Area and Length Preserving Geometric Invariant Scale-Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1995 Jan 1;17(1):67–72.

Published In

IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI

ISSN

0162-8828

Publication Date

January 1, 1995

Volume

17

Issue

1

Start / End Page

67 / 72

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing