Visual search connects physical (offline) objects with (online) digital media. Using objects from the environment, like newspapers, magazines, books and posters, we can retrieve supplemental information from the online world. In this demonstration, we show a framework for delivering visual search services to users of mobile devices. We show how users can point a mobile device at any location in a document, magazine or book to view related, online material on the device. We describe client applications now being deployed for the iPhone and the server architecture used for recognition of scanned images.

A novel super-resolution reconstruction (SRR) framework in magnetic resonance imaging (MRI) is proposed. Its purpose is to produce images of both high resolution and high contrast desirable for image-guided minimally invasive brain surgery. The input data are multiple 2-D multislice inversion recovery MRI scans acquired at orientations with regular angular spacing rotated around a common frequency encoding axis. The output is a 3-D volume of isotropic high resolution. The inversion process resembles a localized projection reconstruction problem. Iterative algorithms for reconstruction are based on the projection onto convex sets (POCS) formalism. Results demonstrate resolution enhancement in simulated phantom studies, and ex vivo and in vivo human brain scans, carried out on clinical scanners. A comparison with previously published SRR methods shows favorable characteristics in the proposed approach.