The popularity of camera phones enables many exciting multimedia applications. In this paper, we present a novel technology and several applications that allow users to interact with paper documents, books, and magazines. This interaction is in the form of reading and writing electronic information, such as images, web urls, video, and audio, to the paper medium by pointing a camera phone at a patch of text on a document. Our application does not require any special markings, barcodes, or watermarks on the paper document. Instead, we propose a document recognition algorithm that automatically determines the location of a patch of text in a large collection of document images given a small document image. This is very challenging because the majority of phone cameras lack autofocus and macro capabilities and they produce low quality images and video. We developed a novel algorithm, Brick Wall Coding (BWC), that performs image-based document recognition using the mobile phone video frames. Given a document patch image, BWC utilizes the layout, i.e. relative locations, of word boxes in order to determine the original file, page, and the location on the page. BWC runs real-time (4 frames per second) on a Treo 700w smartphone with a 312 MHz processor and 64MB RAM. Using our method we can recognize blurry document patch frames that contain as little as 4-5 lines of text and a video resolution as low as 176x144. We performed experiments by indexing 4397 document pages and querying this database with 533 document patches. Besides describing the basic algorithm, this paper also describes several applications that are enabled by mobile phone-paper interaction, such as inserting electronic annotations to paper, using paper as a tangible interface to collect and communicate multimedia data, and collaborative homework.

We propose novel thinning conditions for grayscale 3D images that do not rely on image thresholding or segmentation. The resulting grayscale skeletonization routine extracts a topologically-consistent skeleton of curve-like features at all scales and intensities of the image. Our thinning conditions yield rich structural information that be used to identify and delete low-contrast shape features created by blurring and noise.

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.

Synthetic aperture focusing consists of warping and adding together the images in a 4D light field so that objects lying on a specified surface are aligned and thus in focus, while objects lying of this surface are misaligned and hence blurred. This provides the ability to see through partial occluders such as foliage and crowds, making it a potentially powerful tool for surveillance. If the cameras lie on a plane, it has been previously shown that after an initial homography, one can move the focus through a family of planes that are parallel to the camera plane by merely shifting and adding the images. In this paper, we analyze the warps required for tilted focal planes and arbitrary camera configurations. We characterize the warps using a new rank- 1 constraint that lets us focus on any plane, without having to perform a metric calibration of the cameras. We also show that there are camera configurations and families of tilted focal planes for which the warps can be factorized into an initial homography followed by shifts. This shear-warp factorization permits these tilted focal planes to be synthesized as efficiently as frontoparallel planes. Being able to vary the focus by simply shifting and adding images is relatively simple to implement in hardware and facilitates a real-time implementation. We demonstrate this using an array of 30 videoresolution cameras; initial homographies and shifts are performed on per-camera FPGAs, and additions and a final warp are performed on 3 PCs.