Image and Spatial Data Analysis Group
Mission Statement
The information age we currently live in exists on top a large amount of digital data. Thanks to the web we have access to this data. Thanks to search engines which index this data we can efficiently search it. What many may not realize is that this is only a fraction of the data that could be at our disposal. For millennia humans have recorded information on mediums such as paper. This information exists today in libraries and archives throughout the world with much of it not truly being a part of the information age. Most text we find on the web was entered digitally by a human being who typed it on a keyboard. To go back and do this for all the archived records created before the invention of the computer is an enormous and costly task. A much more realistic and practical means of digitizing this information is to scan it as a digital image. Digitizing old data in this manner however only solves one of our two problems, that of providing access to the data, not searchable access. This problem is exemplified by work currently being undertaken for the 1940s Census data which contains 3.8 million JPEG images. Anyone can download this data and look at it. But then what? Can one find anything by looking through these millions of images containing billions of individual entries?
It is problems like these where the field of Computer Vision can offer some solutions. Though still a relatively young field, the burgeoning amounts of raw image data many organizations, agencies, and companies must deal with today have made even imperfect solutions highly desirable if they allow one to make some sense of large image collections. The Image and Spatial Data Analysis Group (ISDA), formed nearly a decade ago by Dr. Peter Bajcsy and currently led by Dr. Kenton McHenry and Dr. Jong Lee, conducts research and development into Cyberinfrasture involving image and video data. Sifting through the state of the art in Computer Vision the ISDA group applies research to real world problems while striving to build robust multipurpose software to serve a variety of community needs. Our group has and is currently working on providing a framework for searchable access to large collections of handwritten forms, creating suites of semantic keyword extractors from image content, developing tools to aid in the study of group interactions in collections of videos, developing tools to aid in the identification of authorship in old manuscripts, and stitching together large macro images from collections of overlapping photographs. With decades of paper archives in the U.S. alone coupled with low cost ubiquitous digital cameras that we all have today, efforts to unlock large collections of image data is becoming a necessity while also being very much a computational intensive supercomputing problem.
