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

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

van Gennip, Y 
Schönlieb, CB 
Rowland, HM 
Flenner, A 

Abstract

© 2016, Springer Science+Business Media New York. 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.

Description

Keywords

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

Journal Title

Journal of Mathematical Imaging and Vision

Conference Name

Journal ISSN

0924-9907
1573-7683

Volume Title

57

Publisher

Springer Science and Business Media LLC
Sponsorship
Engineering and Physical Sciences Research Council (EP/J009539/1)
Engineering and Physical Sciences Research Council (EP/M00483X/1)
Engineering and Physical Sciences Research Council (EP/N014588/1)
Alan Turing Institute (unknown)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
Engineering and Physical Sciences Research Council (EP/H023348/1)