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

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 by Flenner and Bertozzi 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.

References in corpus (1)

• Mean curvature, threshold dynamics, and phase field theory on finite graphs

Cited by in corpus (7)

• Classification and image processing with a semi-discrete scheme for fidelity forced Allen--Cahn on graphs
• A Graph Framework for Manifold-valued Data
• Graph MBO as a semi-discrete implicit Euler scheme for graph Allen-Cahn flow
• An MBO scheme for minimizing the graph Ohta-Kawasaki functional
• Joint reconstruction-segmentation on graphs
• On the Gamma convergence of functionals defined over pairs of measures and energy-measures
• Semi-supervised Learning for Aggregated Multilayer Graphs Using Diffuse Interface Methods and Fast Matrix Vector Products