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A New Colorimetrically-Calibrated Automated Video-Imaging Protocol for Day-Night Fish Counting at the OBSEA Coastal Cabled Observatory

机译:OBSEA沿海有线天文台昼夜鱼类计数的一种新的比色校准自动视频成像协议

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摘要

Field measurements of the swimming activity rhythms of fishes are scant due to the difficulty of counting individuals at a high frequency over a long period of time. Cabled observatory video monitoring allows such a sampling at a high frequency over unlimited periods of time. Unfortunately, automation for the extraction of biological information (i.e., animals' visual counts per unit of time) is still a major bottleneck. In this study, we describe a new automated video-imaging protocol for the 24-h continuous counting of fishes in colorimetrically calibrated time-lapse photographic outputs, taken by a shallow water (20 m depth) cabled video-platform, the OBSEA. The spectral reflectance value for each patch was measured between 400 to 700 nm and then converted into standard RGB, used as a reference for all subsequent calibrations. All the images were acquired within a standardized Region Of Interest (ROI), represented by a 2 × 2 m methacrylate panel, endowed with a 9-colour calibration chart, and calibrated using the recently implemented “3D Thin-Plate Spline” warping approach in order to numerically define color by its coordinates in n-dimensional space. That operation was repeated on a subset of images, 500 images as a training set, manually selected since acquired under optimum visibility conditions. All images plus those for the training set were ordered together through Principal Component Analysis allowing the selection of 614 images (67.6%) out of 908 as a total corresponding to 18 days (at 30 min frequency). The Roberts operator (used in image processing and computer vision for edge detection) was used to highlights regions of high spatial colour gradient corresponding to fishes' bodies. Time series in manual and visual counts were compared together for efficiency evaluation. Periodogram and waveform analysis outputs provided very similar results, although quantified parameters in relation to the strength of respective rhythms were different. Results indicate that automation efficiency is limited by optimum visibility conditions. Data sets from manual counting present the larger day-night fluctuations in comparison to those derived from automation. This comparison indicates that the automation protocol subestimate fish numbers but it is anyway suitable for the study of community activity rhythms.
机译:由于很难长时间统计高频率的个体,因此对鱼类游泳活动节奏的现场测量很少。有线天文台视频监控允许在无限的时间内进行高频采样。不幸的是,提取生物信息(即每单位时间动物的视觉计数)的自动化仍然是主要的瓶颈。在这项研究中,我们描述了一种新的自动视频成像协议,用于通过比色校准的延时摄影输出中的鱼24小时连续计数,该输出由浅水(20 m深度)有线视频平台OBSEA拍摄。在400至700 nm之间测量每个贴片的光谱反射率值,然后将其转换为标准RGB,用作所有后续校准的参考。所有图像均在2×2 m的甲基丙烯酸酯面板表示的标准化关注区域(ROI)内采集,并具有9色校准图,并使用最近实施的“ 3D薄板样条”翘曲方法进行了校准。为了通过颜色在n维空间中的坐标在数字上定义颜色。由于是在最佳可见性条件下获取的,因此对图像的子集(作为训练集的500幅图像)重复了该操作。通过主成分分析将所有图像加上训练集的图像一起排序,从而可以从908张图像中选择614张图像(67.6%),总计相当于18天(每30分钟一次)。 Roberts运算符(用于图像处理和计算机视觉中的边缘检测)用于突出显示与鱼的身体相对应的高空间颜色渐变区域。将手动计数和视觉计数中的时间序列进行了比较,以进行效率评估。周期图和波形分析输出提供了非常相似的结果,尽管相对于各个节奏​​强度的量化参数是不同的。结果表明,自动化效率受到最佳可见度条件的限制。与自动化相比,手动计数的数据集呈现出更大的昼夜波动。这种比较表明,自动化协议低估了鱼类数量,但无论如何它都适合于研究社区活动节奏。

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