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

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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. ***