IDBR: Development of Tools for Individual Recognition of Animals

IDBR：动物个体识别工具的开发

• 批准号: 0754773
• 负责人: Douglas Bolger
• 金额: $ 21.23万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0754773&HistoricalAwards=false
• 关键词: IDBR; Development; Tools; Individual; Recognition

A grant has been awarded to Drs. Douglas Bolger and Hany Farid of Dartmouth College to develop tools for the individual recognition of animals. The ability to recognize and follow individual animals over space and time is perhaps the most important tool of animal population biology. Recognizing individuals allows researchers to estimate population size and birth and death rates, and quantify social behavior. These parameters form the basis of most pure and applied population biology. Traditionally, this recognition has been accomplished by capturing animals and placing visible and unique marks on them. These methods are known as mark-recapture or mark-resight. The primary limitations on the use of traditional marking techniques are animal welfare, cost and difficulty. One promising non-invasive technique is the use of photographic ?mark? and resight methods. For animals with unique markings, individuals can be photographed (marked) and the images stored in a database. Animals photographed later can then be compared to the image database to determine if that individual had been seen before (a resight) or if it is new to the study. This method has been used manually for the study of relatively small populations such as those of whales. For use in large populations this image matching process needs to be computer-assisted to be feasible.Our project is a collaboration between biology and computer science to develop and test an open-source application for individual recognition of animals. This application will include the following modules (1) an image database for the storing and accessing of individual images; (2) several choices of pattern extraction techniques; and (3) several choices of pattern matching algorithm. This system will process digital photographs of individual animals, efficiently extract the essential pattern information, store this information in a database, and efficiently search the existing database for matching images. The feature detectors to be implemented include the Harris detector, steerable filters, and scale invariant feature transform (SIFT). Pattern recognition algorithms will include nearest neighbor, principal components analysis, linear discriminant analysis, and K-means. The system will be tested against our existing mark-resight photographic database of 8,250 wildebeest images from the Tarangire ecosystem in northern Tanzania. Furthermore, we will capture images of two other uniquely patterned ungulates from the same ecosystem: zebra and giraffe. We will use these data and analyses to estimate population size, survival and recruitment for wildebeest. For giraffe and zebra we will be able to estimate population size. In addition to these three African ungulate populations we will also test this system against two collaborator image databases of spotted salamanders and whale sharks. System performance will be evaluated on the basis of the misidentification error rates that it produces for these test databases, as well as the general adaptability of the system for use with different species.The tools will be developed using common open source applications and will be made available to other researchers through Dartmouth College?s website. In recent years there have been tremendous advances in analytical methods for mark-resight data. These new analytical techniques allow for more accurate parameter estimation and give researchers the ability to rigorously test complex hypotheses. However, the application of these methods has been limited by the relatively small number of populations that have sufficient mark-resight data available. The availability of the tools we create should lead to an increase in at least one to two orders of magnitude in the number of populations that can be monitored and parameterized using photographic mark-resight methodology. This should in turn lead to more informed management and conservation of these animal populations.

达特茅斯学院的道格拉斯博尔格博士和哈尼法里德博士获得了一笔赠款，用于开发识别动物个体的工具。在空间和时间上识别和跟踪个体动物的能力可能是动物种群生物学最重要的工具。 通过识别个体，研究人员可以估计人口规模、出生率和死亡率，并量化社会行为。 这些参数构成了大多数纯粹的和应用的种群生物学的基础。传统上，这种识别是通过捕捉动物并在其上放置可见和独特的标记来完成的。 这些方法被称为标记-再捕获或标记-再捕获。使用传统标记技术的主要限制是动物福利、成本和难度。一个有前途的非侵入性技术是使用摄影？标记？和其他方法。 对于具有独特标记的动物，可以对个体进行拍照（标记）并将图像存储在数据库中。 随后拍摄的动物可以与图像数据库进行比较，以确定该个体是否以前见过（一次）或是否是新的研究对象。这种方法已经被人工用于研究相对较小的种群，如鲸鱼。 为了在大规模人群中使用，这种图像匹配过程需要计算机辅助才能实现。我们的项目是生物学和计算机科学之间的合作，旨在开发和测试一个用于动物个体识别的开源应用程序。 该应用程序将包括以下模块：（1）用于存储和访问单个图像的图像数据库;（2）模式提取技术的几种选择;以及（3）模式匹配算法的几种选择。 该系统将处理单个动物的数字照片，有效地提取基本模式信息，将这些信息存储在数据库中，并有效地搜索现有数据库以找到匹配的图像。待实现的特征检测器包括Harris检测器、可操纵滤波器和尺度不变特征变换（SIFT）。 模式识别算法将包括最近邻、主成分分析、线性判别分析和K均值。该系统将与我们现有的标记-标记图像数据库进行测试，该数据库包含来自坦桑尼亚北方Tarangire生态系统的8,250张角马图像。此外，我们还将拍摄来自同一生态系统的另外两种独特图案的有蹄类动物：斑马和长颈鹿。 我们将使用这些数据和分析来估计角马的种群规模，生存和招募。 对于长颈鹿和斑马，我们将能够估计人口规模。 除了这三个非洲有蹄类动物种群，我们还将测试这个系统对两个合作者的图像数据库的斑点蝾螈和鲸鲨。 系统的性能将进行评估的基础上的错误识别错误率，它产生的这些测试数据库，以及一般的适应性的系统使用不同的species.The工具将开发使用共同的开源应用程序，并将提供给其他研究人员通过达特茅斯学院？的网站。近年来，在标记数据的分析方法方面取得了巨大的进步。 这些新的分析技术允许更准确的参数估计，并使研究人员能够严格测试复杂的假设。然而，这些方法的应用一直受到限制，相对较少的人口有足够的标记-标记数据。我们创建的工具的可用性应导致至少增加一到两个数量级的人口数量，可以使用摄影标记-标记方法进行监测和参数化。 这反过来又会导致更明智的管理和保护这些动物种群。