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Image Analysis and Reconstruction using a Wavelet Transform Constructed from a Reducible Representation of the Euclidean Motion Group
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.ORCID iD: 0000-0002-6096-3648
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands .
2007 (English)In: International Journal of Computer Vision, ISSN 0920-5691, E-ISSN 1573-1405, Vol. 72, no 1, 79-102 p.Article in journal (Refereed) Published
Abstract [en]

Inspired by the early visual system of many mammalians we consider the construction of-and reconstruction from- an orientation score Uf:R2×S1→C as a local orientation representation of an image, f:R2→R . The mapping f↦Uf is a wavelet transform Wψ corresponding to a reducible representation of the Euclidean motion group onto L2(R2) and oriented wavelet ψ∈L2(R2) . This wavelet transform is a special case of a recently developed generalization of the standard wavelet theory and has the practical advantage over the usual wavelet approaches in image analysis (constructed by irreducible representations of the similitude group) that it allows a stable reconstruction from one (single scale) orientation score. Since our wavelet transform is a unitary mapping with stable inverse, we directly relate operations on orientation scores to operations on images in a robust manner.

Furthermore, by geometrical examination of the Euclidean motion group G=R2R×T , which is the domain of our orientation scores, we deduce that an operator Φ on orientation scores must be left invariant to ensure that the corresponding operator W−1ψΦWψ on images is Euclidean invariant. As an example we consider all linear second order left invariant evolutions on orientation scores corresponding to stochastic processes on G. As an application we detect elongated structures in (medical) images and automatically close the gaps between them.

Finally, we consider robust orientation estimates by means of channel representations, where we combine robust orientation estimation and learning of wavelets resulting in an auto-associative processing of orientation features. Here linear averaging of the channel representation is equivalent to robust orientation estimation and an adaptation of the wavelet to the statistics of the considered image class leads to an auto-associative behavior of the system.

Place, publisher, year, edition, pages
2007. Vol. 72, no 1, 79-102 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-41574DOI: 10.1007/s11263-006-8894-5Local ID: 57835OAI: oai:DiVA.org:liu-41574DiVA: diva2:262428
Note

The original publication is available at www.springerlink.com: Remco Duits, Michael Felsberg, Gösta Granlund and Bart M. ter Haar Romeny, Image Analysis and Reconstruction using a Wavelet Transform Constructed from a Reducible Representation of the Euclidean Motion Group, 2007, International journal of computer vision., (72), 1, 79-102. http://dx.doi.org/10.1007/s11263-006-8894-5 Copyright: Springer Science Business Media http://www.springerlink.com/

Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-05-04

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Felsberg, MichaelGranlund, Gösta

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