CISE Postdoctoral Program: Postdoctoral Research Associate in Experimental Science: Applying Computer Vision Methods to Image Databases

CISE博士后项目：实验科学博士后研究员：将计算机视觉方法应用于图像数据库

• 批准号: 9503994
• 负责人: Hanan Samet
• 金额: $ 6.82万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-04-01 至 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9503994&HistoricalAwards=false
• 关键词: CISE; Postdoctoral; Program; Research; Associate

9503994 Samet One of the emerging areas of research is the integration of databases containing nontraditional data with conventional databases. New indexing methods are required in order to store and retrieve nontraditional data efficiently. These methods must capture the nature of the data and sort on this information. In this research the methods for integrating symbolic images into relational database systems will be extended to a more general case of images. The problem is how to match the stored image with an observed image that is presented to the database as a query, but is taken from a different viewpoint. A solution based on utilizing methods that are used in computer vision to solve these problems, and integrating these results into a relational (or any other) database system will be investigated. The solution is based on the use of geometric invariants. An additional research goal that extends this work into other nontraditional data types such as video data will also be performed. As video is becoming a common data type in information systems, data management systems should support this new kind of data. The important issue is how to extract contextual, spatial and temporal information from the video sequences in order to index on video data based on its contents. A solution that is based on gathering information about methods that are used for this purpose in computer vision, and adapting them so they can be used in the framework of database systems will be outlined. In particular, using a system the recognizes facial expressions from video sequences of a human face is suggested as a testbed. ***

9503994 SAMET新兴研究领域之一是将包含非传统数据的数据库与常规数据库相结合。为了高效地存储和检索非传统数据，需要新的索引方法。这些方法必须捕获数据的性质并对此信息进行排序。在这项研究中，将把符号图像集成到关系数据库系统中的方法扩展到更一般的图像情况。问题是如何将存储的图像与观察到的图像进行匹配，该图像作为查询呈现给数据库，但从不同的角度获取。将研究一种基于利用计算机视觉中使用的方法来解决这些问题，并将这些结果集成到关系(或任何其他)数据库系统中的解决方案。该解决方案基于几何不变量的使用。还将执行另一个研究目标，将这项工作扩展到其他非传统数据类型，如视频数据。随着视频成为信息系统中的一种常见数据类型，数据管理系统应该支持这种新的数据类型。重要的问题是如何从视频序列中提取上下文、空间和时间信息，以便根据视频数据的内容对其进行索引。将概述一种解决方案，其基础是收集关于计算机视觉中用于这一目的的方法的信息，并对其进行调整，以便能够在数据库系统的框架中使用。特别是，建议使用从人脸视频序列识别面部表情的系统作为试验床。***