Distortion Estimation Through Explicit Modeling of the Refractive Surface

Precise calibration is a must for high reliance 3D computer vision algorithms. A challenging case is when the camera is behind a protective glass or transparent object: due to refraction, the image is heavily distorted; the pinhole camera model alone can not be used and a distortion correction step is required. By directly modeling the geometry of the refractive media, we build the image generation process by tracing individual light rays from the camera to a target. Comparing the generated images to their distorted - observed - counterparts, we estimate the geometry parameters of the refractive surface via model inversion by employing an RBF neural network. We present an image collection methodology that produces data suited for finding the distortion parameters and test our algorithm on synthetic and real-world data. We analyze the results of the algorithm.Comment: Accepted to ICANN 201

Surface Reconstruction of the Surgical Field From Stereoscopic Microscope Views in Neurosurgery

Introduction In neurosurgery, guidance systems, which correlate patient imaging data to the surgical field using tracked probes or surgical microscopes, have become standard equipment. The most advanced systems dispose of an additional head-up display showing relevant information on a two-dimensional graphical overlay directly within one of the oculars of the microscope. Our experience has shown that to take full advantage of augmented reality, more complex colour and stereoscopic displays are needed for a better understanding and interpretation of the overlaid information [1]. Therefore, this basic overlay will be replaced in future by more sophisticated three-dimensional displays being able to render stereoscopic views of a virtual scene. Today's augmented reality, where the real scene's information is enhanced with additional information, might be backed by augmented virtuality [2,3]. Augmented virtuality fuses multimodal scene information acquired during planning with real-time i