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

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Zitation

Calatroni, L., van Gennip, Y., Schonlieb, C. B., Rowland, H. M., & Flenner, A. (2017). Graph clustering, variational image segmentation methods and hough transform scale detection for object measurement in images. Journal of Mathematical Imaging and Vision, 57(2), 269-291. doi:10.1007/s10851-016-0678-0.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002D-C9FA-2
Zusammenfassung
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 Bertozzi and Flenner (Multiscale Model Simul 10(3):1090-1118, 2012) 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.