Interactive Vision Tools to Index and Search Biological Image Databases for Natural Resource Conservation

用于索引和搜索生物图像数据库以保护自然资源的交互式视觉工具

• 批准号: 0640529
• 负责人: Kevin McGarigal
• 金额: $ 75.46万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0640529&HistoricalAwards=false
• 关键词: Interactive; Vision; Tools; Index; Search

The University of Massachusetts-Amherst is awarded a grant to develop tools for employing digital photography to identify and monitor movements of individual animals in the environment. The project will be carried out in collaboration with the Massachusetts Institute of Technology. The tools developed in this project will make it possible for biologists to query image data bases on a scale that would otherwise be impossible using manual approaches. This project will develop standards for image acquisition and management, tools for rapid segmentation, lighting normalization, and recognition algorithms for individual animal identification. The proposed methodology focuses on recognition of surface markings and targets an "assisted" rather than fully automated identification process. The methodology is motivated by the design and use of generic visual features that can be adapted to many recognition tasks using statistical learning and user provided relevance feedback. The method will be developed and evaluated using three species of amphibians but is expected to scale to other organisms. The ability to identify individual animals is essential to many research endeavors in biology. In field studies, for example, biologists rely on multiple observations over time and space to quantify population sizes, demographic rates, habitat utilization, movement rates and distances. Addressing these types of issues is often critical not only to basic ecological and evolutionary studies, but also to inform conservation planning or natural resource management decisions, in many cases involving rare and endangered species. The emergence of cheap, high quality digital photography makes it possible to document the movements of large numbers of individual animals in species in which traditional methods are not practical.

马萨诸塞大学阿默斯特分校获得了一笔赠款，用于开发利用数字摄影识别和监测环境中单个动物运动的工具。该项目将与马萨诸塞州理工学院合作实施。 在这个项目中开发的工具将使生物学家能够查询图像数据库的规模，否则不可能使用手动方法。该项目将开发图像采集和管理标准、快速分割工具、照明标准化和个体动物识别的识别算法。拟议方法的重点是识别表面标记，目标是“辅助”而不是完全自动化的识别过程。该方法的动机是设计和使用通用的视觉功能，可以适应许多识别任务，使用统计学习和用户提供的相关性反馈。该方法将使用三种两栖动物进行开发和评估，但预计将扩展到其他生物。识别个体动物的能力对于生物学中的许多研究工作是必不可少的。例如，在实地研究中，生物学家依靠时间和空间上的多次观察来量化种群规模、人口统计率、栖息地利用率、移动率和距离。解决这些类型的问题往往不仅对基本的生态和进化研究至关重要，而且对保护规划或自然资源管理决策也至关重要，在许多情况下涉及稀有和濒危物种。廉价、高质量的数码摄影技术的出现，使人们有可能记录下大量动物个体的活动，而传统的方法在这些动物中是不实用的。