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ReScape:用于定量分析的珊瑚礁景观图像转换。

ReScape: transforming coral-reefscape images for quantitative analysis.

机构信息

Institute for Global Ecology, Florida Institute of Technology, Melbourne, FL, 32901, USA.

Department of Computer Science, Florida Institute of Technology, Melbourne, FL, 32901, USA.

出版信息

Sci Rep. 2024 Apr 17;14(1):8915. doi: 10.1038/s41598-024-59123-2.

Abstract

Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm, ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the first-ever ecological application and extension of inverse-perspective mapping-a foundational technique used in the autonomous-driving industry. The ReScape algorithm is composed of seven functions that (1) calibrate the camera lens, (2) remove the inherent lens-induced image distortions, (3) detect the scene's horizon line, (4) remove the camera-roll angle, (5) detect the transformable reef area, (6) detect the scene's perspective geometry, and (7) apply brute-force inverse-perspective mapping. The performance of the ReScape algorithm was evaluated by transforming the perspective of 125 reefscape images. Eighty-five percent of the images had no processing errors and of those, 95% were successfully transformed into top-down views. ReScape was validated by demonstrating that same-length transects, placed increasingly further from the camera, became the same length after transformation. The mission of the ReScape algorithm is to (i) unlock historical information about coral-reef conditions from previously unquantified periods and localities, (ii) enable citizen scientists and recreational photographers to contribute reefscape images to the scientific process, and (iii) provide a new survey technique that can rigorously assess relatively large areas of coral reefs, and other marine and even terrestrial ecosystems, worldwide. To facilitate this mission, we compiled the ReScape algorithm into a free, user-friendly App that does not require any coding experience. Equipped with the ReScape App, scientists can improve the management and prediction of the future of coral reefs by uncovering historical information from reefscape-image archives and by using reefscape images as a new, rapid survey method, opening a new era of coral-reef monitoring.

摘要

自 1885 年首次拍摄到珊瑚礁图像以来,世界各地的人们一直在积累珊瑚礁景观图像,记录珊瑚礁的历史状况。然而,这些无数的珊瑚礁景观图像都存在透视变形问题,这会降低远处分类单元的表观尺寸,使得这些图像无法用于珊瑚礁状况的定量分析。在这里,我们通过开发一种新颖的计算机视觉算法 ReScape 来解决这个长达一个世纪的变形问题,该算法通过将珊瑚礁景观图像转换为俯视视角来消除透视变形,从而使这些图像可用于珊瑚礁状况的定量分析。通过这样做,我们展示了逆向透视映射的首次生态应用和扩展——这是自动驾驶行业中使用的一项基础技术。ReScape 算法由七个功能组成,(1)校准相机镜头,(2)消除固有镜头引起的图像失真,(3)检测场景的地平线,(4)消除相机滚动角度,(5)检测可变形的珊瑚礁区域,(6)检测场景的透视几何,以及(7)应用暴力逆向透视映射。通过转换 125 张珊瑚礁景观图像来评估 ReScape 算法的性能。85%的图像没有处理错误,其中 95%成功转换为俯视视角。通过演示放置在离相机越来越远的相同长度的横切,在转换后变成相同的长度,验证了 ReScape 的有效性。ReScape 算法的任务是(i)从以前无法量化的时期和地点解锁有关珊瑚礁状况的历史信息,(ii)使公民科学家和休闲摄影师能够将珊瑚礁景观图像贡献到科学过程中,以及(iii)提供一种新的调查技术,可以严格评估全球范围内的珊瑚礁以及其他海洋甚至陆地生态系统的相对大面积。为了实现这一任务,我们将 ReScape 算法编译成一个免费的、用户友好的应用程序,不需要任何编码经验。配备 ReScape 应用程序,科学家可以通过从珊瑚礁景观图像档案中挖掘历史信息,以及将珊瑚礁景观图像用作一种新的快速调查方法,来改善珊瑚礁的管理和预测未来,从而开启珊瑚礁监测的新时代。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d9d/11024090/20ba2823e5c6/41598_2024_59123_Fig1_HTML.jpg

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