The Google newspaper search program was launched on September 8, 2008. In this paper, we outline the technology pieces underlying this large and complex project. We have created a production pipeline which takes newspaper microfilms as input and emits individual news articles as output. These articles are then indexed and added to the content base, so that they turn up in response to Google searches. Thus, in response to a Google query ldquoHitler deathrdquo, we are able to show newspaper articles from the very day it was reported, authentic and unbiased by passage of time. Non-uniform illumination, presence of significant noise, tears and scratches in the microfilm image, all pose special challenges for this project. The significant variation of layouts across newspapers and time eras, the variations in font sizes occurring in a single page (which confuses the OCR engine) compound the difficulties. The project is still going on after the initial launch was made (with about 15 million news articles).

We present a technique to linearly estimate the radial distortion of a wide-angle lens given three views of a real-world plane. The approach can also be used with pure rotation as in this case all points appear as lying on a plane. The three views can even be recorded using three different cameras as long as the deviation from the pin-hole model for each camera is distortion along radial lines. We introduce the 1D radial camera which projects scene points onto radial lines and the radial trifocal tensor which encodes the multi-view relations between radial lines. Given at least seven triplets of corresponding points the radial trifocal tensor can be computed linearly. This allows recovery of the radial cameras and the projective reconstruction of the plane up to a two fold ambiguity. This 2D reconstruction is unaffected by radial distortion and can be used in different ways to compute the radial distortion parameters. We propose to use the division model as in this case we obtain a linear algorithm that computes the radial distortion coefficients and the 3 remaining degrees of freedom of the homography relating the reconstructed 2D plane to the undistorted image. Each feature point that has at least one corresponding point yields one linear constraint on those unknowns. Our method is validated on real-world images. We successfully calibrate several wide-angle cameras.

In the first part we discuss the problem of recovering the calibration of a network of pan-tilt-zoom cameras. The intrinsic parameters of each camera over its full range of zoom settings are estimated through a two step procedure. We first determine the intrinsic parameters at the camera's lowest zoom setting very accurately by capturing an extended panorama. The camera intrinsics are then determined at discrete steps in a monotonically increasing zoom sequence that spans the full zoom range of the cameras. Both steps are fully automatic and do not assume any knowledge of the scene structure. We validate our approach by calibrating two different types of pan tilt zoom cameras placed in an outdoor environment. We also show the high-resolution panoramic mosaics built from the images captured during this process. The second section deals with the calibration of omnidirectional cameras. A broad class of both central and non-central cameras, such as fish-eye and catadioptric cameras, can be reduced to 1D radial cameras under the assumption of known center of radial distortion. We study the multi-view geometry of 1D radial cameras. For cameras in general configuration, we introduce a quadrifocal tensor. From this tensor a metric reconstruction of the 1D cameras as well as the observed features can be obtained. In a second phase this reconstruction can then be used as a calibration object to estimate a non-parametric non-central model for the cameras. We study some degenerate cases, including pure rotation. In the case of a purely rotating camera we obtain a trifocal tensor. This allows us to obtain a metric reconstruction of the plane at infinity. Next, we use the plane at infinity as a calibration device to non-parametrically estimate the radial distortion.

We study the multi-view geometry of 1D radial cameras. A broad a class of both central and non-central cameras, such as fish-eye and catadioptric cameras, can be reduced to 1D radial cameras under the assumption of known center of radial distortion. For cameras in general configuration, we introduce a quadrifocal tensor that can be computed linearly from 15 or more features seen in four views. From this tensor a metric reconstruction of the 1D cameras as well as the observed features can be obtained. In a second phase this reconstruction can then be used as a calibration object to estimate a non-parametric non-central model for the cameras. We study some degenerate cases, including pure rotation. In the case of a purely rotating camera we obtain a trifocal tensor that can be estimated linearly from 7 points in three views. This allows us to obtain a metric reconstruction of the plane at infinity. Next, we use the plane at infinity as a calibration device to non-parametrically estimate the radial distortion. We demonstrate the results of our approach on real and synthetic images