1. The Collaborative Imaging Grid Paul Javid, Kurtis Heimerl A collaborative research environment enabling Researchers to learn from images when computer vision cannot

2. What is the problem with computer vision?  Computer Vision is getting better at seeing patterns in images  Face Recognition for Security (Is this person Kurtis?)  Traffic Monitoring (Are there many cars on the road?)  But we are no where near the capabilities of the human eye!  Medical research (Does this lung have lung cancer?)  Spyware detection (Is this image spyware?)  Requires Human Pattern Recognition!

3. The Solution  Create a research tool allowing researchers to aggregate human knowledge about digital images.  Images are coupled with metadata  Metadata contains additional information about the images  Researchers have access to “database” which contains all images and their coupled metadata  Creates the “world-wide-web” of human knowledge about images.

4. Example application scenario  100 Cancer Researchers have knowledge about what a cancerous lung looks like.  In order for such information to be shared ubiquitously:  When researcher identifies cancerous lung image, they add that image and its metadata to database  Metadata contains information about patient history, age, etc.  Researcher then may analyze other images and contribute to their metadata  Allows other researchers to search, group, edit, and categorize images in database based on their metadata, thereby furthering cancer research

5. How are we going to do this?  Clients have access to database via a web-browser.  Outputs DHTML  Server side uses GNU’s Tomcat webserver and the Spring Framework.  Toolkits implemented in Java!  Metadata stored as XML files.  Velocity toolkit to generate HTML for client  Clients log in via web browser to Tomcat webserver which contains database of images and coupled metadata (XML Files). Server exports HTML for clients viewing

6. ConclusionConclusion  Obvious demand for Collaborative Imaging Grid in various disciples - biotechnology, computer vision, etc.  Easy to Implement  Well known implementation language (Java)  Easy integration between XML metadata, webserver, and client side browser.  We have done this before!