An astronomical pattern-matching algorithm for computer-aided identification of whale sharks Rhincodon typus
An astronomical pattern-matching algorithm for computer-aided identification of whale sharks Rhincodon typus
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DOI：
10.1111/j.1365-2664.2005.01117.x
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发表时间：
2005-12-01
影响因子：
5.7
通讯作者：
Norman, B
中科院分区：
文献类型：
--
作者：
Arzoumanian, Z;Holmberg, J;Norman, B
1. 保护政策的制定在很大程度上依赖于人口统计学、生物学和生态学知识，而这些知识对于濒危物种来说往往是难以捉摸的。对丰度、存活率和生活史参数的基本估计可以通过给予足够大样本的标记和再捕获研究获得。个人的照片识别是一种既定的标记和重新捕获技术，但由于在大数据集中进行视觉识别的难以管理的任务，它的全部潜力很少得到利用。我们描述了一种通过其自然表面“斑点”颜色的数字模式匹配来识别个体鲸鲨的新技术。过去，在鲸鲨侧面照片中捕捉到的斑点图案，连同疤痕和其他标记，被用来通过眼睛进行识别。我们采用了一种计算机算法，使这一过程自动化，这种算法最初是在天文学中开发的，用于比较夜空图像中的恒星模式。在使用一组先前识别的鲨鱼图像的测试中，我们的方法在90%以上的情况下正确匹配了显示相同模式的成对。从一个更大的以前未识别的图像库中，迄今为止已经产生了100多个新的匹配图像。我们的技术是强大的，因为误报的发生率很低，而无法匹配同一鲨鱼的图像主要是由于在超过30度的斜角下获得的照片中的缩短。我们描述了模式匹配算法的实现，对其有效性的估计，将其纳入新的ECOCEAN鲸鲨照片识别库，并对其进一步改进进行了展望。我们还对跨越广泛地理和时间跨度识别单个鲨鱼的能力的生物学和保护意义发表评论。合成与应用。一种自动照片识别技术已经开发出来，可以对斑点动物进行有效的“虚拟标记”。模式匹配软件是在一个基于web的库中实现的，该库是为管理一般的偶遇照片和派生数据而创建的。综合能力已经证明了鲸鲨斑点模式长期识别的可靠性，并承诺新的生态见解。预计将这项技术推广到其他物种，通过更好地了解生活史、人口趋势和迁徙路线，以及诸如开发影响和野生动物保护区的有效性等生态因素，对管理和保护带来好处。
1. The formulation of conservation policy relies heavily on demographic, biological and ecological knowledge that is often elusive for threatened species. Essential estimates of abundance, survival and life-history parameters are accessible through mark and recapture studies given a sufficiently large sample. Photographic identification of individuals is an established mark and recapture technique, but its full potential has rarely been exploited because of the unmanageable task of making visual identifications in large data sets.2. We describe a novel technique for identifying individual whale sharks Rhincodon typus through numerical pattern matching of their natural surface 'spot' colourations. Together with scarring and other markers, spot patterns captured in photographs of whale shark flanks have been used, in the past, to make identifications by eye. We have automated this process by adapting a computer algorithm originally developed in astronomy for the comparison of star patterns in images of the night sky.3. In tests using a set of previously identified shark images, our method correctly matched pairs exhibiting the same pattern in more than 90% of cases. From a larger library of previously unidentified images, it has to date produced more than 100 new matches. Our technique is robust in that the incidence of false positives is low, while failure to match images of the same shark is predominantly attributable to foreshortening in photographs obtained at oblique angles of more than 30 degrees.4. We describe our implementation of the pattern-matching algorithm, estimates of its efficacy, its incorporation into the new ECOCEAN Whale Shark Photo-identification Library, and prospects for its further refinement. We also comment on the biological and conservation implications of the capability of identifying individual sharks across wide geographical and temporal spans.5. Synthesis and applications. An automated photo-identification technique has been developed that allows for efficient 'virtual tagging' of spotted animals. The pattern-matching software has been implemented within a Web-based library created for the management of generic encounter photographs and derived data. The combined capabilities have demonstrated the reliability of whale shark spot patterns for long-term identifications, and promise new ecological insights. Extension of the technique to other species is anticipated, with attendant benefits to management and conservation through improved understanding of life histories, population trends and migration routes, as well as ecological factors such as exploitation impact and the effectiveness of wildlife reserves.