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Image Processing for Ice Floe Analyses in Broken-ice Model Testing

机译:浮冰模型测试中浮冰分析的图像处理

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摘要

The ice floe shape and size distribution are important ice parameters in ice-structure analyses. Before performing an analysis at full scale, the dynamic positioning (DP) experiments in model ice at the Hamburg Ship Model Basin (HSVA) allow for the testing of relevant image processing algorithms. A complete overview image of the ice floe distribution in the ice tank was generated from the experiments. An image processing method based on a gradient vector flow (GVF) snake and a distance transform is proposed to identify individual ice floes. Ice floe characteristics such as position, area, and size distribution are obtained. A model of the managed ice field's configuration, including identification of overlapping floes, is also proposed for further studies in ice-force numerical simulations. Finally, the proposed algorithm is applied to an ice surveillance video to further illustrate its applicability to ice management.
机译:浮冰的形状和大小分布是冰结构分析中重要的冰参数。在进行全面分析之前,在汉堡船模型盆地(HSVA)的模型冰中进行动态定位(DP)实验,可以测试相关的图像处理算法。通过实验生成了冰罐中浮冰分布的完整概图。提出了一种基于梯度矢量流(GVF)蛇形和距离变换的图像处理方法,以识别单个浮冰。获得了浮冰的特性,例如位置,面积和大小分布。还提出了受管冰场构造的模型,包括识别重叠的絮凝物,以用于冰力数值模拟的进一步研究。最后,将所提出的算法应用于冰监视视频,以进一步说明其在冰管理中的适用性。

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