We present a flexible interactive 3D morpho-kinematical modeling application for astrophysics. Compared to other systems, our application reduces the restrictions on the physical assumptions, data type, and amount that is required for a reconstruction of an object's morphology. It is one of the first publicly available tools to apply interactive graphics to astrophysical modeling. The tool allows astrophysicists to provide a priori knowledge about the object by interactively defining 3D structural elements. By direct comparison of model prediction with observational data, model parameters can then be automatically optimized to fit the observation. The tool has already been successfully used in a number of astrophysical research projects.
Other algorithms that are more oriented towards high visual quality than physical accuracy of the results are based on symmetry constraints ( @cite_21 @cite_25 , Lin t u et al. @cite_7 @cite_29 , @cite_30 ). Many astronomical nebulae show an inherent spherical or axial symmetry due to their evolution from more or less symmetrical sources. This symmetry assumption may be used to reconstruct the missing spatial dimension @cite_15 .
@cite_30 @cite_7 @cite_29 @cite_21 @cite_15 @cite_25
2151637322 2108210481 2145457898 2076617402
Determining the three-dimensional structure of distant astronomical objects is a challenging task, given that terrestrial observations provide only one viewpoint. For this task, bipolar planetary nebulae are interesting objects of study because of their pronounced axial symmetry due to fundamental physical processes. Making use of this symmetry constraint, we present a technique to automatically recover the axisymmetric structure of bipolar planetary nebulae from two-dimensional images. With GPU-based volume rendering driving a non-linear optimization, we estimate the nebulaýs local emission density as a function of its radial and axial coordinates, and we recover the orientation of the nebula relative to Earth. The optimization refines the nebula model and its orientation by minimizing the differences between the rendered image and the original astronomical image. The resulting model enables realistic 3D visualizations of planetary nebulae, e.g. for educational purposes in planetarium shows. In addition, the recovered spatial distribution of the emissive gas allows validating computer simulation results of the astrophysical formation processes of planetary nebulae. From our terrestrially confined viewpoint, the actual three-dimensional shape of distant astronomical objects is, in general, very challenging to determine. For one class of astronomical objects, however, spatial structure can be recovered from conventional 2D images alone. So-called planetary nebulae (PNe) exhibit pronounced symmetry characteristics that come about due to fundamental physical processes. Making use of this symmetry constraint, we present a technique to automatically recover the axisymmetric structure of many planetary nebulae from photographs. With GPU-based volume rendering driving a nonlinear optimization, we estimate the nebula's local emission density as a function of its radial and axial coordinates and we recover the orientation of the nebula relative to Earth. The optimization refines the nebula model and its orientation by minimizing the differences between the rendered image and the original astronomical image. The resulting model allows creating realistic 3D visualizations of these nebulae, for example, for planetarium shows and other educational purposes. In addition, the recovered spatial distribution of the emissive gas can help astrophysicists gain deeper insight into the formation processes of planetary nebulae. Distant astrophysical objects like planetary nebulae can normally only be observed from a single point of view. Assuming a cylindrically symmetric geometry, one can nevertheless create 3D models of those objects using tomographic methods. We solve the resulting algebraic equations efficiently on graphics hardware. Small deviations from axial symmetry are then corrected using heuristic methods, because the arising 3D models are, in general, no longer unambiguously defined. We visualize the models using real-time volume rendering. Models for actual planetary nebulae created by this approach match the observational data acquired from the earth’s viewpoint, while also looking plausible from other viewpoints for which no experimental data is available. In this paper, we propose a new, compact, 4D spatio-temporal “Nebula” feature to improve expression and facial movement analysis performance. Given a spatio-temporal volume, the data is voxelized and fit to a cubic polynomial. A label is assigned based on the principal curvature values, and the polar angles of the direction of least curvature are computed. The labels and angles for each feature are used to build a histogram for each region of the face. The concatenated histograms from each region give us our final feature vector. This feature description is tested on the posed expression database BU-4DFE and on a new 4D spontaneous expression database. Various region configurations, histogram sizes, and feature parameters are tested, including a non-dynamic version of the approach. The LBP-TOP approach on the texture image as well as on the depth image is also tested for comparison. The onsets of the six canonical expressions are classified for 100 subjects in BU-4DFE, while the onset, offset, and non-existence of 12 Action Units (AUs) are classified for 16 subjects from our new spontaneous database. For posed expression recognition, the Nebula feature approach shows improvement over LBPTOP on the depth images and significant improvement over the non-dynamic 3D-only approach. Moreover, the Nebula feature performs better for AU classification than the compared approaches for 11 of the AUs tested in terms of accuracy as well as Area Under Receiver Operating Characteristic Curve (AUC).