Uncertainty estimates related to the position of image features are seeing increasing use in several computer vision problems. Many of these have been recast from standard least squares model fitting to techniques that minimize the Mahalanobis distance, which weighs each error vector by covariance of the observations. These include structure from motion and traditional geometric camera calibration. Uncertainty estimates previously derived for the case of corner localization are based on implicit assumptions that preclude sophisticated image noise models. Uncertainties associated with these features tend to be over estimated. In this work, we introduce a new formulation for feature location uncertainty that supports arbitrary pixel covariance to derive a more accurate positional uncertainty estimate. The method is developed and evaluated in the case of a traditional interest operator that is in widespread use. Results show that uncertainty estimates based on this new formulation better reflect the error distribution in feature location.

We present a quality assessment procedure for correspondence estimation based on geometric coherence rather than ground truth. The procedure can be used for performance evaluation of correspondence extraction schemes developed by researchers, as well as for online learning and adaptation aimed at better system performance. A very important aspect of the proposed procedure is that it considers uncertainty in the correspondence extraction, and encourages the evaluated methods to deal correctly with uncertainty. Other important strengths of the procedure are that it does not use any manual work, and that it does not put any strong constraints on the scene, but rather relies on geometric coherence in the motion. Thanks to these strengths, it can therefore be used with large amounts of real, potentially application specific data, or even data acquired during system operation. In the evaluation the correspondence extractor is handled as a black box producing a probability distribution for the local motion vector between a pair of image patches. The procedure is therefore quite general. We are making the evaluation procedure available for public use.

Front-projection displays are a cost-effective and increasingly popular method for large format visualization and immersive rendering of virtual models. New approaches to projector tiling, automatic calibration, and color balancing have made multiprojector display systems feasible without undue infrastructure changes and maintenance. As a result, front-projection displays are being used to generate seamless, visually immersive worlds for virtual reality and visualization applications with reasonable cost and maintenance overhead. However, these systems suffer from a fundamental problem: Users and other objects in the environment can easily and inadvertently block projectors, creating shadows on the displayed image. Shadows occlude potentially important information and detract from the sense of presence an immersive display may have conveyed. We introduce a technique that detects and corrects shadows in a multiprojector display while it is in use. Cameras observe the display and compare observations with an expected image to detect shadowed regions. These regions are transformed to the appropriate projector frames, where corresponding pixel values are increased and/or attenuated. In display regions where more than one projector contributes to the image, shadow regions are eliminated.

We are developing a novel display system that physically realizes a sampling of the light field emitted by a three-dimensional scene. An array of projectors, each with a two-dimensional framebuffer, populates the 4D space of the light field. The view of the scene is simultaneously correct for all head positions within a volume. This eliminates the need for head tracking, produces binocular disparities without the need for glasses, and supports any number of viewers. Input to our display could be streamed from a light field sensor [1], or can be efficiently rendered in parallel using a cluster of standard computer graphics pipelines. For static scenes, no run-time rendering is necessary. We demonstrate the feasibility of the approach using a prototype cluster of projectors.

Front-projection display environments suffer from a fundamental problem: users and other objects in the environment can easily and inadvertently block projectors, creating shadows on the displayed image. We introduce a technique that detects and corrects transient shadows in a multi-projector display. Our approach is to minimize the difference between predicted (generated) and observed (camera) images by continuous modification of the projected image values for each display device. We speculate that the general predictive monitoring framework introduced here is capable of addressing more general radiometric consistency problems. Using an automatically-derived relative position of cameras and projectors in the display environment and a straightforward color correction scheme, the system renders an expected image for each camera location. Cameras observe the displayed image, which is compared with the expected image to detect shadowed regions. These regions are transformed to the appropriate projector frames, where corresponding pixel values are increased. In display regions where more than one projector contributes to the image, shadow regions are eliminated. We demonstrate an implementation of the technique in a multiprojector system.