Adaptive Application of Feature Detection Operators Based on Image Variance

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

It is well known that the strength of a feature in an image may depend on the scale at which the appropriate detection operator is applied. It is also the case that many features in images exist significantly over a limited range of scales, and, of particular interest here, that the most salient scale may vary spatially over the feature. Hence, when designing feature detection operators, it is necessary to consider the requirements for both the systematic development and adaptive application of such operators over scale- and image-domains. We present an overview to the design of scalable derivative edge detectors, based on the finite element method, that addresses the issues of method and scale-adaptability. The finite element approach allows us to formulate scalable image derivative operators that can be implemented using a combination of piecewise-polynomial and Gaussian basis functions. The general adaptive technique may be applied to a range of operators. Here we evaluate the approach using image gradient operators, and we present comparative qualitative and quantitative results for both first and second order derivative methods.
LanguageEnglish
Pages2403-2406
JournalPattern Recognition
Volume37
Issue number12
DOIs
Publication statusPublished - Dec 2004

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operators
finite element method
polynomials
gradients
requirements
detectors

Keywords

  • Adaptive filtering
  • Feature detection
  • Scale
  • Image variance

Cite this

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Adaptive Application of Feature Detection Operators Based on Image Variance. / Coleman, SA; Scotney, BW; Herron, MG.

Vol. 37, No. 12, 12.2004, p. 2403-2406.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Adaptive Application of Feature Detection Operators Based on Image Variance

AU - Coleman, SA

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AB - It is well known that the strength of a feature in an image may depend on the scale at which the appropriate detection operator is applied. It is also the case that many features in images exist significantly over a limited range of scales, and, of particular interest here, that the most salient scale may vary spatially over the feature. Hence, when designing feature detection operators, it is necessary to consider the requirements for both the systematic development and adaptive application of such operators over scale- and image-domains. We present an overview to the design of scalable derivative edge detectors, based on the finite element method, that addresses the issues of method and scale-adaptability. The finite element approach allows us to formulate scalable image derivative operators that can be implemented using a combination of piecewise-polynomial and Gaussian basis functions. The general adaptive technique may be applied to a range of operators. Here we evaluate the approach using image gradient operators, and we present comparative qualitative and quantitative results for both first and second order derivative methods.

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