Imaging is at the core of many scientific discoveries, with information captured in terms of raw pixel intensities and in multiple channels for color or hyperspectral imagery. This project investigates the generation of probabilistic measurements and quantified uncertainties from image analysis methods, pattern classification methods to extract information that can be stored as probabilistic feature tables, and new approaches to visualization of probabilistic information, for the purpose of uncertainty management in the analysis of complex image data for scientific and engineering applications.

Grant: NSF OIA-0941717
Researchers: Emre Sargin and Pradeep Koulgi

The objectives here are algorithms for efficient, interactive, and approximate or exact similarity search in high-dimensional data sets via approaches that draw inspiration not just from classical data management but also  from several other disciplines including optimization, information theory, pattern recognition, and signal compression. The project eschews the perspective of treating the compression for database storage, and efficient retrieval problems separately, to demonstrate the myriad benefits of jointly optimizing both aspects.

Grant: NSF IIS-0329267
Researchers: Kumar Viswanatha, Jayanth Nayak, and Sharadh Ramaswamy