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

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

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.-B., 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: https://nottingham-repository.worktribe.com/output/799559
• Publisher URL: http://link.springer.com/article/10.1007%2Fs10851-016-0678-0
• Additional Information: The final publication is available at link.springer.com via http://dx.doi.org/10.1007/s10851-016-0678-0
• Contract Date: Sep 23, 2016