Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images
This work addresses a practical problem in image analysis for fields like zoology and medicine, but it appears incremental as it combines existing methods without introducing major new paradigms.
The paper tackles the problem of scale detection in images containing a region of interest and a measurement tool like a ruler, using graph clustering and Hough transform methods to achieve object measurement in applications such as zoology, medicine, and archaeology.
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.