In applied science and engineering contexts, ocean free-surface wave-field characterization is an ongoing research area relevant to many diverse applications. The use of optical means for this characterization, in particular, is an appealing solution due to the promise of relatively high spatial resolution and modest cost (i.e., applying off-the-shelf still cameras or video cameras). This work investigates the validity of a previously published algorithm related to visible-wavelength ocean free-surface wave-field videos. The first part of the algorithm uses the video content to determine scale from a single-perspective, uncalibrated video. The second part of the algorithm applies the video content to determine sea-state characteristics, such as dominant wave period and wavelength. Due to the complications of obtaining well-characterized real-world data sets, algorithms proposed within the literature are not always validated experimentally, but the scientific community relies on the process of verification and validation to build confidence in emerging scientific contributions. This work addresses the need to assess the performance of the existing algorithm through validation experiments. Video recordings of approximate deep-water waves are collected in a controlled indoor environment to experimentally validate the algorithm, and a suite of postprocessing parameters is explored to identify the algorithm's sensitivities for a chosen unit problem. Recommendations for the algorithm parameter selection and an assessment of the algorithm efficacy are offered for researchers interested in the implementation of this algorithm.

中文翻译：

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一种用于识别尺度和海况特征的单视图图像序列算法的验证实验

在应用科学和工程环境中，海洋自由表面波场表征是一个与许多不同应用相关的持续研究领域。由于具有相对高的空间分辨率和适中的成本（即，应用现成的静态相机或视频相机），因此使用光学装置进行这种表征尤其是一种吸引人的解决方案。这项工作调查了先前发布的与可见波长海洋自由表面波场视频相关的算法的有效性。算法的第一部分使用视频内容来确定单视角、未校准视频的比例。算法的第二部分应用视频内容来确定海况特征，例如主波周期和波长。由于获得充分表征的现实世界数据集的复杂性，文献中提出的算法并不总是经过实验验证，但科学界依靠验证和验证过程来建立对新兴科学贡献的信心。这项工作解决了通过验证实验评估现有算法性能的需要。在受控室内环境中收集近似深水波的视频记录以对算法进行实验验证，并探索一套后处理参数来确定算法对选定单元问题的敏感性。为对该算法的实现感兴趣的研究人员提供算法参数选择和算法功效评估的建议。