Dytkowicz Margarete, Tania Marcello, Hinds Rachel, Megill William M, Buttschardt Tillmann K, Rosell Frank
FabLab Blue, Faculty of Technology and Bionics University of Applied Sciences Kleve Germany.
Research Group Applied Landscape Ecology and Ecological Planning, Institute of Landscape Ecology WWU Münster Münster Germany.
Ecol Evol. 2024 Feb 13;14(2):e10922. doi: 10.1002/ece3.10922. eCollection 2024 Feb.
Individual recognition of animals is an important aspect of ecological sciences. Photograph-based individual recognition options are of particular importance since these represent a non-invasive method to distinguish and identify individual animals. Recent developments and improvements in computer-based approaches make possible a faster semi-automated evaluation of large image databases than was previously possible. We tested the Scale Invariant Feature Transform (SIFT) algorithm, which extracts distinctive invariant features of images robust to illumination, rotation or scaling of images. We applied this algorithm to a dataset of 800 tail pattern images from 100 individual Eurasian beavers () collected as part of the Norwegian Beaver Project (NBP). Images were taken using a single-lens reflex camera and the pattern of scales on the tail, similar to a human fingerprint, was extracted using freely accessible image processing programs. The focus for individual recognition was not on the shape or the scarring of the tail, but purely on the individual scale pattern on the upper (dorsal) surface of the tail. The images were taken from two different heights above ground, and the largest possible area of the tail was extracted. The available data set was split in a ratio of 80% for training and 20% for testing. Overall, our study achieved an accuracy of 95.7%. We show that it is possible to distinguish individual beavers from their tail scale pattern images using the SIFT algorithm.
动物个体识别是生态科学的一个重要方面。基于照片的个体识别方法尤为重要，因为这些方法代表了一种区分和识别个体动物的非侵入性方法。基于计算机的方法的最新发展和改进使得对大型图像数据库进行比以前更快的半自动评估成为可能。我们测试了尺度不变特征变换（SIFT）算法，该算法可提取图像中对光照、旋转或缩放具有鲁棒性的独特不变特征。我们将此算法应用于作为挪威海狸项目（NBP）一部分收集的100只欧亚海狸的800张尾部图案图像数据集。使用单反相机拍摄图像，并使用免费的图像处理程序提取尾部鳞片的图案，类似于人类指纹。个体识别的重点不是尾巴的形状或疤痕，而是纯粹在尾巴上表面（背部）的个体鳞片图案。图像是从离地面两个不同高度拍摄的，并提取了尾巴尽可能大的区域。可用数据集按80%用于训练和20%用于测试的比例进行划分。总体而言，我们的研究准确率达到了95.7%。我们表明，使用SIFT算法可以从海狸的尾部鳞片图案图像中区分个体海狸。
