Accurate measurement of the position of features in an image is subject to a fundamental compromise: The features must be both small, to limit the effect of nonlinear distortions, and large, to limit the effect of noise and discretization. This constrains both the accuracy and the robustness of image measurements, which play an important role in geometric camera calibration as well as in all subsequent measurements based on that calibration. In this paper, we present a new geometric camera calibration technique that exploits the complete camera model during the localization of control markers, thereby abolishing the marker size compromise. Large markers allow a dense pattern to be used instead of a simple disc, resulting in a significant increase in accuracy and robustness. When highly planar markers are used, geometric camera calibration based on synthetic images leads to true errors of 0.002 pixels, even in the presence of artifacts such as noise, illumination gradients, compression, blurring, and limited dynamic range. The camera parameters are also accurately recovered, even for complex camera models.

A new spectral model of the return signal from a LIDAR Doppler wake vortex detector is proposed. It has been experimentally discovered during ground-based and flight test campaigns but suffered a lack of theoretical evidence. Using high resolution fluid simulations of wake vortices, we highlight the physical meaning of this model. Comparisons with the traditional single Gaussian model show the superiority of this new approach is consistent with previous experimental results.

This paper describes a system based on a 360-degree camera with a single microphone that detects speech activity in a roundtable context for the purpose of estimating discourse participation status information for each member present. We have obtained 97 % accuracy in detecting participants and have shown that the use of non-verbal and backchannel speech information is a useful indicator of participant status in a discourse. Index Terms: round-table meetings, image processing, nonverbal behaviour, speech activity, discourse management

The paper introduces a correlation-based method for the three-dimensional reconstruction of scenes from a multi-camera imaging system. Our technique is to cast the matching and reconstruction problems into a single 3D process that uses perspective distortions to retrieve directly, in a dense fashion, the 3D planes locally tangent to the scene. Avoiding image assumptions like fronto-parallelism, block shapes, perspective distortion models or camera models, our method only needs a local planarity hypothesis of the scene.