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Multi-frequency transformation for edge detection

Published: 26 November 2012 Publication History

Abstract

This paper investigates the use of a multi-frequency transformation for edge detection. Most edge detectors are good at detecting non-texture edges, but have problems with texture edges. In order to detect texture edges, prior knowledge is usually required to avoid heavy computational cost. In this study, a fast and simple transformation based on multi-frequencies is proposed to improve detection performance and the relevant analysis for proper responses on texture and non-texture edges is given. The experimental results show that a classical edge detector improves detection performance after using the proposed transformation based on multi-frequencies, and the detection result from the edge detector using the transformation is better than the detection result from some popular feature extraction techniques, such as extraction based on Gaussian gradients, histogram gradients, and surround suppression.

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cover image ACM Other conferences
IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand
November 2012
547 pages
ISBN:9781450314732
DOI:10.1145/2425836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • HRS: Hoare Research Software Ltd.
  • Google Inc.
  • Dept. of Information Science, Univ.of Otago: Department of Information Science, University of Otago, Dunedin, New Zealand

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2012

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IVCNZ '12
Sponsor:
  • HRS
  • Dept. of Information Science, Univ.of Otago
IVCNZ '12: Image and Vision Computing New Zealand
November 26 - 28, 2012
Dunedin, New Zealand

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Overall Acceptance Rate 55 of 74 submissions, 74%

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