This paper presents a new approach of combining real video and synthetic objects. The purpose of this work is to use the proposed technology in the fields of advanced animation, virtual reality, games, and so forth. Computer graphics has been used in the fields previously mentioned. Recently, some applications have added real video to graphic scenes for the purpose of augmenting the realism that the computer graphics lacks in. This approach called augmented or mixed reality can produce more realistic environment than the entire use of computer graphics. Our approach differs from the virtual reality and augmented reality in the manner that computer-generated graphic objects are combined to 3-D structure extracted from monocular image sequences. The extraction of the 3-D structure requires the estimation of 3-D depth followed by the construction of a height map. Graphic objects are then combined to the height map.The realization ofour proposed approach is carried out in the followingsteps: (1) We derive 3-D structure from test image sequences. The extraction of the 3-D structure requires the estimation of depth and the construction of a hciglit map. Due to the contents of the test sequence, the height map represents the 3-D structure. (2) The htight map is modeled by Delaunay triangulation or Bezier surface and each planar surface is texture-mapped (3) Finally, graphic objects are combined to the height map. Because 3-D structure of the height map is already known, Step (3) is easily manipulated. Following this procedure, we produced an animation video demonstrating the combination of the 3-D structure and graphic models. Users can navigate the realistic 3-D world whose associated image is rendered on the display monitor.
We present a recursive estimation technique for recovering FOE (Focus of Expansion) from unreliable motion or optical flow. The estimation of FOE is of importance to the analysis of camera motion, especially, in the case that the camera motion is purely translational. Our work is based on the observation that there is strong dependence between FOE estimation and motion flows. Therefore. as the FOE depends on the motion flow, a good motion flow can be obtained from accurate FOE. We assume that the camera motion is purely translational and there is no object motion in the scene. The technique used for the elimination of unreliable motion flow is orthogonal regression method. We combine FOE estimation with the elimination algorithm of unreliable motion flows. Experiments using both simulation and real scenes show that our proposed method works robustly under the condition that the percentage of outliers is varying between [O%,20%].
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