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

Calatroni, L., van Gennip, Y., Schönlieb, 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.

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EXT509.pdf (Publisher version), 4MB
 
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Calatroni, L., Author
van Gennip, Y., Author
Schönlieb, C. B., Author
Rowland, Hannah M.1, Author              
Flenner, A., Author
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1External Organizations, ou_persistent22              

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 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 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.

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Language(s): eng - English
 Dates: 2016-07-252017
 Publication Status: Published in print
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 Identifiers: Other: EXT509
DOI: 10.1007/s10851-016-0678-0
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Title: Journal of Mathematical Imaging and Vision
Source Genre: Journal
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Publ. Info: Dordrecht, Holland : Kluwer Academic Publishers
Pages: - Volume / Issue: 57 (2) Sequence Number: - Start / End Page: 269 - 291 Identifier: ISSN: 0924-9907
CoNE: https://pure.mpg.de/cone/journals/resource/954925566730