In order to achieve seamless imagery in a planar multi-projector display, geometric distortions and misalignment of images within and across projectors have to be removed. Camera-based calibration methods are popular for achieving this in an automated fashion. Previous methods for geometric calibration fall into two categories: (a) methods that model the geometric function relating the projectors to cameras using simple linear models, like homography, to calibrate the display. These models assume perfect linear devices and cannot address projector nonlinearities, like lens distortions, which are common in most commodity projectors, (b) methods that use piecewise linear approximations to model the relationship between projectors and cameras. These require a dense sampling of the function space to achieve good calibration. In this paper, we present a new closed-form model that relates projectors to cameras in planar multi-projector displays, using rational Bezier patches. This model overcomes the shortcomings of the previous methods by allowing for projectors with significant lens distortion. It can be further used to develop an efficient and accurate geometric calibration method with a sparse sampling of the function.

Multi-projector displays today are automatically registered, both geometrically and photometrically, using cameras. Existing registration techniques assume pre-calibrated projectors and cameras that are devoid of imperfections such as lens distortion. In practice, however, these devices are usually imperfect and uncalibrated. Registration of each of these devices is often more challenging than the multi-projector display registration itself. To make tiled projection-based displays accessible to a layman user we should allow the use of uncalibrated inexpensive devices that are prone to imperfections. In this paper, we make two important advances in this direction. First, we present a new geometric registration technique that can achieve geometric alignment {\em in the presence of severe projector lens distortion} using a relatively inexpensive low-resolution camera. This is achieved via a closed-form model that relates the projectors to cameras, in planar multi-projector displays, using rational Bezier patches. This enables us to geometrically calibrate a 3000 x 2500 resolution planar multi-projector display made of 3 x 3 array of nine severely distorted projectors using a low resolution (640 x 480) VGA camera. Second, we present a photometric self-calibration technique for a projector-camera pair. This allows us to photometrically calibrate the same display made of nine projectors using a photometrically uncalibrated camera. To the best of our knowledge, this is the first work that allows geometrically imperfect projectors and photometrically uncalibrated cameras in calibrating multi-projector displays.

Centralized techniques have been used until now when automatically calibrating (both geometrically and photometrically) large high-resolution displays created by tiling multiple projectors in a 2D array. A centralized server managed all the projectors and also the camera(s) used to calibrate the display. In this paper, we propose an asynchronous distributed calibration methodology via a display unit called the plug-and-play projector (PPP). The PPP consists of a projector, camera, computation and communication unit, thus creating a self-sufficient module that enables an asynchronous distributed architecture for multi-projector displays. We present a single-program-multiple-data (SPMD) calibration algorithm that runs on each PPP and achieves a truly scalable and reconfigurable display without any input from the user. It instruments novel capabilities like adding/removing PPPs from the display dynamically, detecting faults, and reshaping the display to a reasonable rectangular shape to react to the addition/removal/faults. To the best of our knowledge, this is the first attempt to realize a completely asynchronous and distributed calibration architecture and methodology for multi-projector displays.

One way to improve inferences on sensor data is to tune the algorithms through a time-consuming offline procedure. A less expensive and potentially more accurate method is to use an online procedure based on feedback from users, who often know best what the data means to them. We present a method for user-assisted location inference based on 802.11b wireless signal strengths. A user 'corrects' system geolocations by clicking on a map, recording a 'virtual access point' (VAP) at the selected point for future inferences. A best VAP is selected using simple criteria, including the VAP's creator. This permit using other's VAPs while getting their own if one exists, capturing user-specific behavior. The system is also self-maintaining with respect to changing access point deployments. Indoor experiments show very good accuracy for this simple method.