报告题目：Root Mean Square Minimum Distance: a Quality Metric for Localization Nanoscopy Imaging
报告人：Yi Sun, Electrical Engineering Department, The City College of City University of New York
地点：武汉光电国家研究中心A202
时间：周三（1月10日），14:30-16:00
摘要：
A localization algorithm in optical localization nanoscopy plays an important role in obtaining a high-quality image. A universal and objective metric, which is crucial and necessary to evaluate qualities of nanoscopy images and performances of localization algorithms, has not yet been established. The metrics of root mean square error (RMSE or accuracy), precision, recall, and Jaccard index (JAC) are currently used in the literature but in certain conditions fail in distinguishing qualities of different nanoscopy images. In this paper, the root mean square minimum distance (RMSMD) is proposed as a quality metric for localization nanoscopy images. Its properties are analyzed and demonstrated by examples which also show its advantages over RMSE, precision, recall and JAC. As a universal and objective metric, the RMSMD can be broadly applied to applications that need to measure average, local, and mutual fitness of two sets of points.
报告人简介：
孙毅博士获得上海交通大学电子工程学士和硕士学位，明尼苏达大学电子工程博士学位，现为纽约城市大学城市学院电子工程系副教授，终身教职，哥伦比亚大学兼职副教授。孙教授的研究集中在系统建模，参数估值，算法发展，性能分析和信息理论，其应用领域包括信号与图像处理，模式识别，人工神经网，无线通信与网络，机器人视觉，医学图像处理，癌症与心血管疾病诊断，发表了一百多篇同行审批的学术论文。孙教授现在的研究主要集中在超高分辨率纳米光学图像分析及其生物医学应用，以及亲密关系的模型建立。孙教授是纽约城市大学城市学院纳米光学图像实验室主任，IEEE高级会员。
