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

TL;DR

This paper presents a novel image scale detection method combining graph-based segmentation, matrix completion, and Hough transform, applied to real-world measurement tasks in zoology, medicine, and archaeology.

Contribution

It introduces a discrete graph-based segmentation approach with matrix techniques and a Hough transform for scale detection in images with measurement tools.

Findings

Effective scale detection in images with measurement tools

Application to diverse real-world measurement tasks

Improved segmentation performance with matrix techniques

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.