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Automated digital individual identification system with an application to the northern leopard frog Lithobates pipiens.

机译：自动化的数字个人识别系统，可用于北方豹蛙Lithobates pipiens。

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摘要

Identification of individual animals is needed for studying population demography, movement patterns and animal behavior. Animals with unique body markings (e.g., coloration or stripes) are easily identified by the human eye from photographs. However, automating this human ability with software is a complex task, compounded by various interdependent issues. This dissertation investigates critical issues of automating individual identification of northern leopard frogs Lithobates pipiens using images of their dorsal spot pattern. A standard image acquisition guideline was established to obtain high-quality photos. To identify individuals, a fingerprint area with the dorsal spot pattern is obtained from frog images. Image processing steps include fingerprint mapping, contrast normalization, spot extraction, and morphological operations. The spot pattern is represented with numerical features that form the basis for pattern comparison. Similarity measures were defined to compare spot patterns. A pattern recognition algorithm was developed to identify individual northern leopard frogs. The algorithm consists of multiple reduction steps that reduce false matching images at each step to return the top ten closest matches. An "Automated Animal Digital Identification System" (AADIS) was designed to register and identify individuals in the database. The system returns the top ten ranked individuals as closest matches along with a classification of these into probable versus not so probable categories. This classification serves as an indication whether the query individual is in the database or not. The system's performance was tested on different size datasets using random simulations. In a dataset of 200 individuals, with a total of 854 images, the system identified 95% of the true matches in the top ten and classified 87% of the true matches as probable in the top ten. Depending on the discriminators, the system classified 77-84% of individuals new to the database as not so probable matches. The developed pattern recognition algorithm may be adapted to other spotted animals by either adding or removing reduction steps suitable for the animal identification. AADIS is open-source software with a graphical user interface and a database. The image pre-processing steps take an average of 37 seconds for a trained user.
机译：研究种群人口统计学，运动方式和动物行为时，需要识别单个动物。具有独特身体标记（例如，颜色或条纹）的动物很容易被人眼从照片中识别出来。但是，使用软件自动实现这种人的能力是一项复杂的任务，并且会遇到各种相互依赖的问题。本文研究了北豹蛙Lipibates pipiens的背斑点模式图像自动个体识别的关键问题。建立了标准图像获取指南以获取高质量的照片。为了识别个体，从青蛙图像获得具有背侧斑点图案的指纹区域。图像处理步骤包括指纹映射，对比度归一化，斑点提取和形态学操作。点图案用数字特征表示，这些特征构成图案比较的基础。定义相似性度量以比较斑点模式。开发了一种模式识别算法来识别单个北方豹蛙。该算法由多个缩小步骤组成，这些步骤会在每个步骤中缩小错误的匹配图像，以返回最接近的十大匹配项。设计了“自动动物数字识别系统”（AADIS）来注册和识别数据库中的个人。系统将排名最靠前的十个人作为最接近的匹配项，并将其分类为可能类别与不太可能类别。此分类表示查询个体是否在数据库中。使用随机模拟在不同大小的数据集上测试了系统的性能。在200个个体的数据集中，总共854张图像中，系统识别出前十名中95％的真实匹配，并将87％的真实匹配分类为前十名可能。根据区分者，系统将数据库新手的77-84％归类为不太可能的匹配项。通过添加或删除适合动物识别的简化步骤，可以将开发的模式识别算法应用于其他斑点动物。 AADIS是带有图形用户界面和数据库的开源软件。对于经过培训的用户，图像预处理步骤平均需要37秒。

著录项

  • 作者

    Kelly, Oksana Vladimirovna.;

  • 作者单位

    Idaho State University.;

  • 授予单位 Idaho State University.;
  • 学科 Biology Ecology.;Engineering System Science.;Engineering Computer.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 170 p.
  • 总页数 170
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 宗教史、宗教地理;
  • 关键词

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