Collaborative Research: ITR: Interactive Software Systems for Expert-Assisted Image Analysis and Classification of Aquatic Particles

合作研究：ITR：用于专家辅助图像分析和水生颗粒分类的交互式软件系统

• 批准号: 0325018
• 负责人: Mark Benfield
• 金额: $ 24.7万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-11-01 至 2010-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325018&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Interactive; Software

This project involves research on methodologies and algorithms for the automated and semi-automated classification and counting of particles in the upper ocean. The work will harness ideas from computer vision, machine learning, and image analysis to extract features from several different types of optical instruments used to study planktonic marine organisms and detritus, and then classify them so that they can be automatically recognized, sorted and counted. The goal is to develop systems that will automatically classify the majority of marine particles and flag the unusual or difficult-to-recognize features for human interpretation. Feature classification systems will be developed around three different classes of instruments that yield images of three different types of particles: epifluorescence microscopy (bacteria and nanoplankton), FlowCam (phytoplankton including types responsible for harmful algal blooms), in situ cameras (zooplankton and detritus). A variety of image classification methods will be investigated including ones based on neural networks and support vector machines. Reference images will be made from phytoplankton from the collection of the Center for Culture of Marine Phytoplankton and from plankton in samples taken from the Gulf of Maine. Images recorded in past expeditions and on at-sea expeditions funded elsewhere will also be used. As part of the project, a pattern recognition package will be added to the Phytopia educational multi-media CD-ROM used in schools and colleges.

该项目涉及对上层海洋颗粒进行自动和半自动分类和计数的方法和算法的研究。 这项工作将利用计算机视觉、机器学习和图像分析的思想，从用于研究浮游海洋生物和碎屑的几种不同类型的光学仪器中提取特征，然后对它们进行分类，以便自动识别、分类和计数。 目标是开发能够自动对大多数海洋颗粒进行分类并标记不寻常或难以识别的特征以供人类解释的系统。 特征分类系统将围绕三种不同类型的仪器开发，这些仪器产生三种不同类型颗粒的图像：落射荧光显微镜（细菌和纳米浮游生物）、FlowCam（浮游植物，包括导致有害藻华的类型）、原位相机（浮游动物和碎屑）。 将研究各种图像分类方法，包括基于神经网络和支持向量机的方法。 参考图像将由海洋浮游植物培养中心收集的浮游植物和从缅因湾采集的浮游植物样本制成。 过去的探险和其他地方资助的海上探险记录的图像也将被使用。 作为该项目的一部分，模式识别包将被添加到学校和学院使用的 Phytopia 教育多媒体 CD-ROM 中。