In this article, we introduce a method to apply ideas from electrostatics to parameterize the open space around an object. By simulating the object as a virtually charged conductor, we can define an object-centric coordinate system which we call Electric Coordinates. It parameterizes the outer space of a reference object in a way analogous to polar coordinates. We also introduce a measure that quantifies the extent to which an object is wrapped by a surface. This measure can be computed as the electric flux through the wrapping surface due to the electric field around the charged conductor. The electrostatic parameters, which comprise the Electric Coordinates and flux, have several applications in computer graphics, including: texturing, morphing, meshing, path planning relative to a target object, mesh parameterization, designing deformable objects, and computing coverage. Our method works for objects of arbitrary geometry and topology, and thus is applicable in a wide variety of scenarios.
Abstract. We first consider network security services and then review threats, vulnerabilities and failure modes. This review is based on standard texts, using well-known concepts, categorizations, and methods, e.g. risk analysis using asset-based threat profiles and vulnerability profiles (attributes). The review is used to construct a framework which is then used to define an extensible ontology for network security attacks. We present a conceptualization of this ontology in figure 1.
Estimation of the point light source position in the scene enhances the experience for augmented reality. The intensity image and depth information from the RGB-D camera allows estimation of the point light source position in a scene, where our approach does not need any probe objects or other measuring devices. The approach uses the Lambertian reflectance model, where the RGB-D camera provides the image and the surface normals and the remaining unknowns are the albedo and light parameters (light intensity and direction). In order to determine the light parameters, we assume that segments with a similar colour have the same albedo, which allows us to find the point light source that explains the illumination in the scene. The performance of this method is evaluated on multiple scenes, where a single light bulb is used to illuminate the scene. In this case, the average error in the angle between the true light position vector and our estimate is around 10 degrees. This allows realistic rendering of synthetic objects into the recorded scene, which is used to improve the experience of augmented reality.
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