4.5 Article

Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images

期刊

JOURNAL OF MATHEMATICAL IMAGING AND VISION
卷 57, 期 2, 页码 269-291

出版社

SPRINGER
DOI: 10.1007/s10851-016-0678-0

关键词

Graph clustering; Discrete Ginzburg-Landau functional; Image segmentation; Scale detection; Hough transform

资金

  1. UK Engineering and Physical Sciences Research Council (EPSRC) [EP/H023348/1]
  2. EPSRC [EP/J009539/1, EP/M00483X/1]
  3. ONR grant [N0001415WX01350]
  4. Royal Society [RG64240]
  5. British Ecology Society [BES2322]
  6. Junior Research Fellowship from Churchill college
  7. Institute Research Fellowship at the Institute of Zoology
  8. Department of Zoology at the University of Cambridge
  9. Churchill College, Cambridge
  10. EPSRC [EP/J009539/1, EP/M00483X/1, EP/N014588/1] Funding Source: UKRI
  11. Alan Turing Institute [TU/B/000071] Funding Source: researchfish
  12. Engineering and Physical Sciences Research Council [EP/N014588/1, EP/M00483X/1, EP/J009539/1] Funding Source: researchfish

向作者/读者索取更多资源

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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