Previous methods of volume rendering are very slow and thus impractical. We present volume rendering based on marching cubes iso-surfacing and transfer function. For an iso-surface, we divide the voxels into logical c...
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Previous methods of volume rendering are very slow and thus impractical. We present volume rendering based on marching cubes iso-surfacing and transfer function. For an iso-surface, we divide the voxels into logical cubes according to a predefined threshold value, the tangent planes and normal vectors at each voxel are calculated and the normal vectors are orientated to the outside of surface based on wide first searching (WFS), and the 3D surface model is then obtained using marching cubes. Transfer function is used to specify the optical properties for volume rendering technology. We employ a 2D function. The end-user interacts with a set of direct manipulation widgets (triangles and rectangles). Each widget precisely corresponds to a different material and widgets are blended automatically to compute an overall transfer function. Compared to traditional techniques, the overall specification process takes a fraction of the time.
The necessary and sufficient condition for the existence of prewavelets with finite decomposition and finite reconstruction is presented. Especially when r = 1, we show that {φj(x-k) | 1 &le j &le r, k ∈ s} ...
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The necessary and sufficient condition for the existence of prewavelets with finite decomposition and finite reconstruction is presented. Especially when r = 1, we show that {φj(x-k) | 1 &le j &le r, k ∈ s} is shift-orthogonal if there exist prewavelets with finite decomposition and finite reconstruction, i.e., non-trivial prewavelets with finite decomposition and finite reconstruction does not exist. As an example, when r = 2 we construct a prewavelet with finite decomposition and finite reconstruction where the scale function is not shift-orthogonal, which demonstrate that there exist vector prewavelets with finite decomposition and finite reconstruction.
This paper presents a fast method to construct the simplified terrain model with the multi-resolution. In this method, we adopt the normal quad-tree hierarchy to subdivide the original terrain surface into the multi- ...
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This paper presents a fast method to construct the simplified terrain model with the multi-resolution. In this method, we adopt the normal quad-tree hierarchy to subdivide the original terrain surface into the multi- resolution levels. For the sake of implementing the compressive storage and the efficient index for the elevation data, a connotative hierarchy is proposed and its corresponding index strategies are deduced. On the basis of the connotative hierarchy, we focus on resolving the crack problem of the simplified model. We firstly create the space filling curve during the simplification process to accelerate the search for the potential cracks. Afterwards, the different approaches are utilized to process the cracks in terms of the dissimilar terrain features. For this reason, we put forward an evaluation function that can selfadaptively identify the terrain feature according to the normal vector angle of the adjacent nodes in the connotative hierarchy. Consequently, there are not redundant triangles in our seamless multi-resolution terrain model. The proposed approaches are experimented on the real data and the results show that our method is relatively efficient and robust. Besides, the seamless simplified model has less number of triangles than the other common algorithm.
The improvement of text categorization by statistical methods can be performed from two main directions, namely the feature selection and the evaluation of characteristic weights. In this paper, we propose an enhanced...
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The improvement of text categorization by statistical methods can be performed from two main directions, namely the feature selection and the evaluation of characteristic weights. In this paper, we propose an enhanced text categorization method based on a modified mutual information algorithm and evaluation algorithm of characteristic weights which improves both aspects. The proposed method is applied to the benchmark test set Reuters-21578 Top10 to examine its effectiveness. Numerical results show that the precision, the recall and the value of F1 of the proposed method are all superior to those of existing conventional methods.
A well-known drawback in the least squares support vector machine (LS-SVM) is that the sparseness is lost. In this study, an effective pruning algorithm is developed to deal with this problem. To avoid solving the pri...
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A well-known drawback in the least squares support vector machine (LS-SVM) is that the sparseness is lost. In this study, an effective pruning algorithm is developed to deal with this problem. To avoid solving the primal set of linear equations, the bottom to the top strategy is adopted in the proposed algorithm. During the training process of the algorithm, the chunking incremental and decremental learning procedures are used alternately. A small support vector set, which can cover most of the information in the training set, can be formed adaptively. Using the support vector set, one can construct the final classifier. In order to test the validation of the proposed algorithm, it has been applied to five benchmarking UCI datasets. In order to show the relationships among the chunking size, the number of support vector machine, the training time, and the testing accuracy, different chunking sizes are tested. The experimental results show that the proposed algorithm can adaptively obtain the sparse solutions without almost losing generalization performance when the chunking size is equal to 2, and also its training speed is much faster than that of the sequential minimal optimization (SMO) algorithm. The proposed algorithm can also be applied to the least squares support vector regression machine as well as LS-SVM classifier.
In this paper, we describe a fast semi-automatic segmentation algorithm. A nodes aggregation method is proposed for improving the running time and a Graph-Cuts method is used to model the segmentation problem. The who...
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Sequential pattern mining is one of the most important fields in data mining. In this paper, we propose a novel algorithm FSPAN (Fast Sequential Pattern mining algorithm) to do the sequence mining. FSPAN can mine all ...
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Identification of Transcription Factor Binding Sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a nove...
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In this paper, we present an efficient method for detecting collisions between highly deformable objects, which is a combination of newly developed stochastic method and Particle Swarm Optimization (PSO) algorithm. Fi...
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In this paper, we investigate the deficiency of Goyal and Egenhofer's method for modeling cardinal directional relations between simple regions and provide the computational model based on the concept of mathemati...
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