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Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images

Calatroni, Luca; van Gennip, Yves; Schönlieb, Carola-Bibiane; Rowland, Hannah M.; Flenner, Arjuna

Authors

Luca Calatroni calatroni@dima.unige.it

Yves van Gennip y.vangennip@nottingham.ac.uk

Carola-Bibiane Schönlieb cbs31@cam.ac.uk

Hannah M. Rowland hr325@cam.ac.uk

Arjuna Flenner arjuna.flenner@navy.mil



Abstract

We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e.g. a ruler). For the segmentation part, we focus on the graph based method presented in which reinterprets classical continuous Ginzburg-Landau minimisation models in a totally discrete framework. To overcome the numerical difficulties due to the large size of the images considered we use matrix completion and splitting techniques. The scale on the measurement tool is detected via a Hough transform based algorithm. The method is then applied to some measurement tasks arising in real-world applications such as zoology, medicine and archaeology.

Citation

Calatroni, L., van Gennip, Y., Schönlieb, C., Rowland, H. M., & Flenner, A. (in press). Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images. Journal of Mathematical Imaging and Vision, https://doi.org/10.1007/s10851-016-0678-0

Journal Article Type Article
Acceptance Date Jul 11, 2016
Online Publication Date Jul 25, 2016
Deposit Date Sep 23, 2016
Publicly Available Date Sep 23, 2016
Journal Journal of Mathematical Imaging and Vision
Print ISSN 0924-9907
Electronic ISSN 1573-7683
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1007/s10851-016-0678-0
Keywords Graph clustering, Discrete Ginzburg–Landau functional, Image segmentation, Scale detection, Hough transform
Public URL http://eprints.nottingham.ac.uk/id/eprint/37060
Publisher URL http://link.springer.com/article/10.1007%2Fs10851-016-0678-0
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information The final publication is available at link.springer.com via http://dx.doi.org/10.1007/s10851-016-0678-0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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