A Shearlet Approach to Edge Analysis and Detection

It is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges. In this paper, we propose a novel approach based on the shearlet transform: a multiscale directional transform with a greater ability to localize distributed discontinuities such as edge...

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Bibliographic Details
Published in:IEEE transactions on image processing 2009-05, Vol.18 (5), p.929-941
Main Authors: Sheng Yi, Labate, D., Easley, G.R., Krim, H.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Application software
Applied sciences
Computer vision
Corners
Curvelets
Design engineering
Discontinuity
edge detection
Exact sciences and technology
Feature extraction
Image analysis
Image edge detection
Image processing
Information, signal and communications theory
Mathematical models
Miscellaneous
Optimization
Pattern recognition
shearlets
Signal processing
singularities
Telecommunications and information theory
Three dimensional displays
Transforms
Wavelet
Wavelet analysis
Wavelet transforms
wavelets
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Summary:It is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges. In this paper, we propose a novel approach based on the shearlet transform: a multiscale directional transform with a greater ability to localize distributed discontinuities such as edges. Indeed, unlike traditional wavelets, shearlets are theoretically optimal in representing images with edges and, in particular, have the ability to fully capture directional and other geometrical features. Numerical examples demonstrate that the shearlet approach is highly effective at detecting both the location and orientation of edges, and outperforms methods based on wavelets as well as other standard methods. Furthermore, the shearlet approach is useful to design simple and effective algorithms for the detection of corners and junctions.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2009.2013082