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视点选取为了提供给用户较好的观察位置,涉及到视点质量好坏的评估。提出了粒子群优化算法的三维可视化最佳视点选取方法。通过采用图像信息熵与图像边缘熵进行视点质量的评估,通过多目标智能优化方法选取视点。基本流程是由初始视点集开始,通过编码、粒子评价和粒子更新等操作寻找最佳视点,这是一个多次迭代的过程,直至找到满意的视点或者达到迭代最大代数。实验表明,该方法可行有效,能自动完成最佳视点的选取,有效地减少了人工试探选取次数。
Viewpoint selection In order to provide the user with a better view of the location, it involves the quality of the viewpoint of the assessment. A method of selecting the best viewpoint of 3D visualization based on Particle Swarm Optimization (PSO) algorithm is proposed. The viewpoint quality is evaluated by using image information entropy and image edge entropy, and the viewpoint is selected by the multi-objective intelligent optimization method. The basic flow starts with the initial set of viewpoints and looks for the best viewpoints through coding, particle evaluation, and particle update operations, which is a multi-iteration process until a satisfactory viewpoint is found or the maximum algebra of iteration is reached. Experiments show that this method is feasible and effective, can automatically select the best viewpoint, effectively reducing the number of manual test.