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Viewpoint Selection Based on NM-PSO for Volume Rendering

机译:基于NM-PSO对卷渲染的观点选择

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To improve the efficiency and the intelligent level, this paper proposed a novel method of viewpoint selection based on the hybrid NM-PSO algorithm for volume rendering. It constructed the viewpoint quality evaluation function in the form of entropy by utilizing the luminance and structure features of the two-dimensional projected image of volume data. During the process of volume rendering, the hybrid NM-PSO algorithm intended to locate the globally optimal viewpoint and/or a set of the optimized viewpoints automatically and intelligently. The experimental results show that this method avoids redundant interactions and evidently improves the efficiency of volume rendering. The optimized viewpoints can rapidly focus on the important structural features or the region of interest in volume data and exhibit definite correlation with the perception character of human visual system. Compared with the methods based on PSO or NM simplex search, our method has the better performance of convergence rate, convergence accuracy and robustness.
机译:为了提高效率和智能级别,本文提出了一种基于混合NM-PSO算法的观点选择的新方法。它通过利用卷数据的二维投影图像的亮度和结构特征来构建熵形式的视点质量评估功能。在体积渲染过程中,混合NM-PSO算法旨在自动且智能地定位全局最佳视点和/或一组优化的视点。实验结果表明,该方法避免了冗余相互作用,显然提高了体积渲染的效率。优化的观点可以迅速关注体积数据的重要结构特征或感兴趣区域,并与人类视觉系统的感知特征表现出明确的相关性。与基于PSO或NM Simplex搜索的方法相比,我们的方法具有更好的收敛速度,收敛准确性和鲁棒性能。

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