Line enhancement and completion via linear left-invariant scale spaces on SE(2)

R. Duits, E.M. Franken

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10 Citaten (Scopus)
1 Downloads (Pure)

Samenvatting

From an image we construct an invertible orientation score, which provides an overview of local orientations in an image. This orientation score is a function on the group SE(2) of both positions and orientations. It allows us to diffuse along multiple local line segments in an image. The transformation from image to orientation score amounts to convolutions with an oriented kernel rotated at multiple angles. Under conditions on the oriented kernel the transform between image and orientation score is unitary. This allows us to relate operators on images to operators on orientation scores in a robust way such that we can deal with crossing lines and orientation uncertainty. To obtain reasonable Euclidean invariant image processing the operator on the orientation score must be both left invariant and non-linear. Therefore we consider non-linear operators on orientation scores which amount to direct products of linear left-invariant scale spaces on SE(2). These linear left-invariant scale spaces correspond to well-known stochastic processes on SE(2) for line completion and line enhancement and are given by group convolution with the corresponding Green’s functions. We provide the exact Green’s functions and approximations, which we use together with invertible orientation scores for automatic line enhancement and completion.
Originele taal-2Engels
TitelScale Space and Variational Methods in Computer Vision (Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009, Proceedings)
RedacteurenX.-C. Tai, K. Morken, M. Lysaker, K.A. Lie
Plaats van productieBerlin
UitgeverijSpringer
Pagina's795-807
ISBN van geprinte versie978-3-642-02255-5
DOI's
StatusGepubliceerd - 2009

Publicatie series

NaamLecture Notes in Computer Science
Volume5567
ISSN van geprinte versie0302-9743

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  • Citeer dit

    Duits, R., & Franken, E. M. (2009). Line enhancement and completion via linear left-invariant scale spaces on SE(2). In X-C. Tai, K. Morken, M. Lysaker, & K. A. Lie (editors), Scale Space and Variational Methods in Computer Vision (Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009, Proceedings) (blz. 795-807). (Lecture Notes in Computer Science; Vol. 5567). Springer. https://doi.org/10.1007/978-3-642-02256-2_66