Automated Visual Fin Identification of Individual Great White Sharks
Automated Visual Fin Identification of Individual Great White Sharks
复制标题
DOI：
10.1007/s11263-016-0961-y
复制
发表时间：
2017-05-01
影响因子：
19.5
通讯作者：
Burghardt, Tilo
中科院分区：
文献类型：
--
作者：
Hughes, Benjamin;Burghardt, Tilo
本文讨论了从背鳍图像中自动识别大白鲨个体的方法。我们提出了一个计算机视觉照片识别系统，并报告了对数千张不受约束的鳍图像的数据库的识别结果。据我们所知，这项工作在动物生物识别领域建立了第一个全自动的基于轮廓的视觉ID系统。提出的方法将鱼翅视为无纹理、柔性和部分遮挡的物体，具有各自独特的形状。为了从图像中恢复动物身份，我们首先引入了一种开放轮廓笔划模型，该模型扩展了多尺度区域分割以实现稳健的鳍检测。其次，我们证明了组合的尺度空间选择性指纹可以成功地编码鳍的个性。然后，我们通过将视觉个性嵌入到全局的“鳍空间”中来测量特定物种的视觉个性在鳍轮廓上的分布。利用这一领域，我们最终提出了一种用于个体动物识别的非线性模型，并将所有方法结合到一个细粒度的多实例框架中。我们提供系统评估，将结果与之前的工作进行比较，并详细报告性能和特性。
This paper discusses the automated visual identification of individual great white sharks from dorsal fin imagery. We propose a computer vision photo ID system and report recognition results over a database of thousands of unconstrained fin images. To the best of our knowledge this line of work establishes the first fully automated contour-based visual ID system in the field of animal biometrics. The approach put forward appreciates shark fins as textureless, flexible and partially occluded objects with an individually characteristic shape. In order to recover animal identities from an image we first introduce an open contour stroke model, which extends multi-scale region segmentation to achieve robust fin detection. Secondly, we show that combinatorial, scale-space selective fingerprinting can successfully encode fin individuality. We then measure the species-specific distribution of visual individuality along the fin contour via an embedding into a global 'fin space'. Exploiting this domain, we finally propose a non-linear model for individual animal recognition and combine all approaches into a fine-grained multi-instance framework. We provide a system evaluation, compare results to prior work, and report performance and properties in detail.