A method for image matching from partial blurry images is presented that leverages existing text retrieval algorithms to provide a solution that scales to hundreds of thousands of images. As an initial application, we present a document image matching system in which the user supplies a query image of a small patch of a paper document taken with a cell phone camera, and the system returns a label identifying the original electronic document if found in a previously indexed collection. Experimental results show that a retrieval rate of over 70% is achieved on a collection of nearly 500,000 document pages.

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.

An algorithm is presented that automatically generates ground-truthed video from a symbolic description for an object and a specification for the movement of a handheld video camera around that object. This provides a method to generate large amounts of training and test data for the development of computer vision algorithms. We describe an implementation of this technique for an imaging application in which a cell phone video camera is moved over a paper document. Experimental results demonstrate the similarity of images captured by the real camera to images generated by the proposed technique.