Automated, high-throughput image calibration for parallel-laser photogrammetry
Automated, high-throughput image calibration for parallel-laser photogrammetry
复制标题
用于并行激光摄影测量的自动化高通量图像校准
DOI：
10.1007/s42991-021-00174-7
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发表时间：
2022
影响因子：
1.6
通讯作者：
Reeves, Mark E.
中科院分区：
文献类型：
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作者：
Richardson, Jack L.;Levy, Emily J.;Ranjithkumar, Riddhi;Yang, Huichun;Monson, Eric;Cronin, Arthur;Galbany, Jordi;Robbins, Martha M.;Alberts, Susan C.;Reeves, Mark E.
激光摄影测量作为一种从野生哺乳动物中收集非侵入性体型数据的方法越来越受欢迎。尽管有许多吸引力，但这种方法需要研究人员手动测量（i）平行激光光斑之间的像素距离（激光器间距离），以在图像内产生比例，以及（ii）研究对象身体标志之间的像素距离（标志间距离）。这种手动工作非常耗时，并且会引入人为误差：研究人员测量同一图像两次很少会返回相同的值（导致观察者内误差），当两个研究人员测量同一图像时也是如此（导致观察者间误差）。在这里，我们提出了两个独立的方法，自动化平行激光摄影测量图像的激光间距离测量。一种方法使用Python中的机器学习和图像处理技术，另一种方法使用ImageJ中的图像处理技术。这两种方法都减少了劳动力，提高了精度，而不牺牲精度。我们首先介绍了这两种方法的工作流程。然后，使用野生山地大猩猩和野生萨凡纳狒狒图像的两个并行激光数据集，我们验证了这两种自动化方法相对于手动测量和彼此的精度。我们还估计了采用这些自动化方法时最终身体尺寸估计值（以厘米为单位）的变化减少，因为这些方法没有人为误差。最后，我们强调了每种方法的优势，建议采用其中任何一种方法的最佳实践，并提出并行激光摄影测量数据自动化的未来方向。
Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject’s body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is also the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques inImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data.