research-article

A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves

Authors:

Farzin Mokhtarian,

Alan K. MackworthAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 14, Issue 8

Pages 789 - 805

https://doi.org/10.1109/34.149591

Published: 01 August 1992 Publication History

Abstract

A shape representation technique suitable for tasks that call for recognition of a noisy curve of arbitrary shape at an arbitrary scale or orientation is presented. The method rests on the describing a curve at varying levels of detail using features that are invariant with respect to transformations that do not change the shape of the curve. Three different ways of computing the representation are described. They result in three different representations: the curvature scale space image, the renormalized curvature scale space image, and the resampled curvature scale space image. The process of describing a curve at increasing levels of abstraction is referred to as the evolution or arc length evolution of that curve. Several evolution and arc length evolution properties of planar curves are discussed.

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Index Terms

A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
  2. Computer graphics
    1. Shape modeling
      1. Parametric curve and surface models
      2. Volumetric models

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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 14, Issue 8

August 1992

82 pages

ISSN:0162-8828

Editor:
Anil K. Jain
Michigan State Univ., East Lansing

Issue’s Table of Contents

Copyright © Copyright © 1992 IEEE. All Rights Reserved.

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 August 1992

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Li ZGuo BMeng FJiang B(2024)Fast shape recognition via a bi-level restraint reduction of contour codingThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02940-940:4(2599-2614)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00371-023-02940-9
Jing JGao TZhang WGao YSun C(2023)Image Feature Information Extraction for Interest Point Detection: A Comprehensive ReviewIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.320118545:4(4694-4712)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3201185
Lin XZhou YLiu YZhu C(2023)A dedicated benchmark for contour-based corner detection evaluationImage and Vision Computing10.1016/j.imavis.2023.104716136:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.imavis.2023.104716
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Zheng YGuo BYan YHe W(2019)O2O Method for Fast 2D Shape RetrievalIEEE Transactions on Image Processing10.1109/TIP.2019.291919528:11(5366-5378)Online publication date: 21-Aug-2019
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