Augmented reality is the merging of synthetic sensory information into a user's perception of a real environment. As one of the most important tasks in augmented scene modeling, terrain simplification research has...
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Augmented reality is the merging of synthetic sensory information into a user's perception of a real environment. As one of the most important tasks in augmented scene modeling, terrain simplification research has gained more and more attention. In this paper, we mainly focus on point selection problem in terrain simplification using triangulated irregular network. Based on the analysis and comparison of traditional importance measures for each input point, we put forward a new importance measure based on local entropy. The results demonstrate that the local entropy criterion has a better performance than any traditional methods. In addition, it can effectively conquer the 'short-sight' problem associated with the traditional methods.
Motivated by the need of the numerous real-world applications, 3D object recognition has become an active research field. The representation describes the sensed data and the object models, and it is a key issue in th...
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Motivated by the need of the numerous real-world applications, 3D object recognition has become an active research field. The representation describes the sensed data and the object models, and it is a key issue in the recognition process, which decides the match strategy and the effectiveness and robustness of the recognition system. In this paper, we propose an improved 3D object representation first, which computes the local signatures of a given basis polygon on the surface mesh, and converts the signatures to a 2D array called the distance-angle (DA) images by weighted bilinear interpolation. This representation is adaptive to free-form objects and resistant to occlusion and clutter. Compared with the original representation, it has a more distinct meaning, easier operation, and adaptation to different resolutions and irregular triangle meshes. Secondly, based on the improved representation, a novel 3D recognition algorithm is presented, which has multiresolution mesh based, coarse-to-fine recognition. By matching the DA image of a polygon in the scene surface mesh with the DA images of models at low resolution, a model candidate set is obtained. The set is filtered in the neighborhood of the matched polygons in a high-resolution mesh and verified by the model candidate sets of other polygons. Experiments show that this algorithm needs less computation and is very accurate and robust.
Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult o segment gravel objects. In ...
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Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult o segment gravel objects. In this paper, we develop a partial entropy method and succeed to realize gravel objects segmentation. We give entropy principles and fur calculation methods. Moreover, we use minimum entropy error automaticly to select a threshold to segment image. We introduce the filter method using mathematical morphology. The segment experiments are performed by using different window dimensions for a group of gravel image and demonstrates that this method has high segmentation rate and low noise sensitivity.
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