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首页> 外文期刊>Journal of mathematical imaging and vision >Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images
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Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images

机译:图形聚类,变分图像分割方法和霍夫图像图像对象测量的变换比例检测

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摘要

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.
机译:我们考虑在图像区域与测量工具(例如尺子)一起存在感兴趣区域的尺度检测问题。 对于分割部分,我们专注于Bertozzi和Flenner(MultiScale Model Simul 10(3):1090-1118,2012)的基于图表的方法,该方法在完全离散的框架中重新诠释了经典连续的Ginzburg-Landau最小化模型。 为了克服由于考虑的图像的大尺寸而导致的数值困难,我们使用矩阵完成和分裂技术。 通过基于Hough变换的算法检测测量工具上的比例。 然后将该方法应用于现实世界应用中产生的一些测量任务,例如动物学,医学和考古学。

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