Device Space Design for Efficient Scale-Space Edge Detection
We present a new approach to the computation of scalable image derivative operators, based on the finite element method, that addresses the issues of method, efficiency and scale-adaptability. The design procedure is applied to the problem of approximating scalable differential operators within the framework of Schwartz distributions. Within this framework, the finite element approach allows us to define a device space in which scalable image derivative operators are implemented using a combination of piecewise-polynomial and Gaussian basis functions.
Here we illustrate the approach in relation to the problem of scale-space edge detection, in which significant scale-space edge points are identified by maxima of existing edge-strength measures that are based on combinations of scale-normalised derivatives. We partition the image in order to locally identify approximate ranges of scales within which significant edge points may exist, thereby avoiding unnecessary computation of edge-strength measures across the entire range of scales.
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- Device Space Design for Efficient Scale-Space Edge Detection
- Book Title
- Computational Science — ICCS 2002
- Book Subtitle
- International Conference Amsterdam, The Netherlands, April 21–24, 2002 Proceedings, Part I
- pp 1077-1086
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 4. Faculty of Science, Section Computational Science, University of Amsterdam
- 5. Western Science Center, SHARCNET, University ofWestern Ontario
- 6. Computer Science Department Innovative Computing Laboratory, University of Tennessee
- Author Affiliations
- 7. School of Information and Software Engineering, University of Ulster, Coleraine, Northern Ireland
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