Collaborative Research: A Visual System for Autonomous Foraminifera Identification

合作研究：自主有孔虫识别的视觉系统

• 批准号: 1637039
• 负责人: Edgar Lobaton
• 金额: $ 17.37万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637039&HistoricalAwards=false
• 关键词: Collaborative; Research; Visual; System; Autonomous

The goal of this project is to develop an automated system for identification of foraminifera (single-celled organisms with shells). Currently undergraduate workers are often employed to hand pick several thousands of specimens from ocean sediments for each study. This is tedious and time consuming work. By automating the bulk of the identification process, user expertise can be focused on verification and identification of subtle differences.A visual identification system will be developed in order to automate the identification of target microorganisms. The visual system will incorporate a controllable LED lighting ring used to capture images by illuminating the specimens from several directions, mimicking an important step in the traditional identification process. These images will be used to create a 3D model of the organism in real-time within a second. Computer vision and pattern recognition techniques will be tuned to acceptable recognition rates set by feedback from an expert in paleoceanography who will also provide labeled samples for training and validation. The initial proof of concept study will focus on identifying six species of planktonic foraminifera, and their morphotypes, that are widely used by paleoceanographers.

这个项目的目标是开发一个自动识别有孔虫（带壳的单细胞生物）的系统。目前，本科生经常被雇用从海洋沉积物中手工挑选数千个标本进行每项研究。这是一项乏味而耗时的工作。通过自动化大部分识别过程，用户的专业知识可以集中在细微差异的验证和识别上。将开发一种视觉识别系统，以便自动识别目标微生物。视觉系统将包括一个可控的LED照明环，用于通过从几个方向照亮标本来捕捉图像，模仿传统识别过程中的重要步骤。这些图像将用于在一秒钟内实时创建生物体的3D模型。计算机视觉和模式识别技术将根据古海洋学专家的反馈调整到可接受的识别率，该专家还将提供标记样本用于训练和验证。最初的概念验证研究将集中于确定六种浮游有孔虫及其形态，这些物种被古海洋学家广泛使用。

项目成果

Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance

• DOI: 10.1016/j.marmicro.2019.01.005
• 发表时间: 2019-03-01
• 期刊: MARINE MICROPALEONTOLOGY
• 影响因子: 1.9
• 作者: Mitra, R.;Marchitto, T. M.;Lobaton, E.
• 通讯作者: Lobaton, E.