In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorith...In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.展开更多
彩色点画是一种从视觉上由大量小像素点构建图像的艺术技术,像素个数的多少直接影响着构图的成本。其优化选点构图方法为实现低成本打印提供了一个重要的方式。目前,点画生成存在着多通道采样点难以均匀分布,颜色层次难以兼顾等难点,并...彩色点画是一种从视觉上由大量小像素点构建图像的艺术技术,像素个数的多少直接影响着构图的成本。其优化选点构图方法为实现低成本打印提供了一个重要的方式。目前,点画生成存在着多通道采样点难以均匀分布,颜色层次难以兼顾等难点,并耗费大量的计算成本。对此,提出了一种基于超像素自适应聚类和线性规划最优选点的彩色点画生成方法,该方法在初步超像素划分图像的基础上,使用基于颜色密度峰值的自适应聚类方法得到最佳聚类个数,并进一步划分子聚类,然后根据每个子聚类的颜色均值作为子聚类内部选点的最佳间隔距离,在选点的同时依据SSIM指标,建立目标优化模型,通过数学优化器Gurobi实现模型选点,使点保留最少个数的目标基础上,同时保持聚类内部分布均匀和颜色渐变层次,以提高所生成的点画图像的可视化效果。实验结果表明,本文算法极大地降低了像素个数并在生成的点画的平均结构相似性(mean structural similarity index measure,SSIM)、峰值信噪比(peak signal to noise ratio,PSNR)等评价指标方面均优于当前方法。展开更多
Poisson disk sampling is an important problem in computer graphics and has a wide variety of applications in imaging, geometry, rendering, etc. In this paper, we propose a novel Poisson disk sampling algorithm based o...Poisson disk sampling is an important problem in computer graphics and has a wide variety of applications in imaging, geometry, rendering, etc. In this paper, we propose a novel Poisson disk sampling algorithm based on disk packing. The key idea uses the observation that a relatively dense disk packing layout naturally satisfies the Poisson disk distribution property that each point is no closer to the others than a specified minimum distance, i.e., the Poisson disk radius. We use this property to propose a relaxation algorithm that achieves a good balance between the random and uniform properties needed for Poisson disk distributions. Our algorithm is easily adapted to image stippling by extending identical disk packing to unequal disks. Experimental results demonstrate the efficacy of our approaches.展开更多
基金This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61372168, 61373071, 61372190, and 61331018, the Scientific Research Foundation for the Returned Overseas Chinese Scholars of State Education Ministry of China, the Visual Computing Center at King Abudullah University of Science and Technology （KAUST）, and the Open Funding Project of the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, under Grant Nos. BUAA-VR- 15KF-06 and BUAA-VR-14KF-10.
文摘In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.
文摘彩色点画是一种从视觉上由大量小像素点构建图像的艺术技术,像素个数的多少直接影响着构图的成本。其优化选点构图方法为实现低成本打印提供了一个重要的方式。目前,点画生成存在着多通道采样点难以均匀分布,颜色层次难以兼顾等难点,并耗费大量的计算成本。对此,提出了一种基于超像素自适应聚类和线性规划最优选点的彩色点画生成方法,该方法在初步超像素划分图像的基础上,使用基于颜色密度峰值的自适应聚类方法得到最佳聚类个数,并进一步划分子聚类,然后根据每个子聚类的颜色均值作为子聚类内部选点的最佳间隔距离,在选点的同时依据SSIM指标,建立目标优化模型,通过数学优化器Gurobi实现模型选点,使点保留最少个数的目标基础上,同时保持聚类内部分布均匀和颜色渐变层次,以提高所生成的点画图像的可视化效果。实验结果表明,本文算法极大地降低了像素个数并在生成的点画的平均结构相似性(mean structural similarity index measure,SSIM)、峰值信噪比(peak signal to noise ratio,PSNR)等评价指标方面均优于当前方法。
基金supported in part by National Natural Science Foundation of China (Nos. 61202147 and 61272243)Shandong Province Natural Science Foundation (No. ZR2012FQ026)Fundamental Research Funds for the Central Universities (No. 20720140520)
文摘Poisson disk sampling is an important problem in computer graphics and has a wide variety of applications in imaging, geometry, rendering, etc. In this paper, we propose a novel Poisson disk sampling algorithm based on disk packing. The key idea uses the observation that a relatively dense disk packing layout naturally satisfies the Poisson disk distribution property that each point is no closer to the others than a specified minimum distance, i.e., the Poisson disk radius. We use this property to propose a relaxation algorithm that achieves a good balance between the random and uniform properties needed for Poisson disk distributions. Our algorithm is easily adapted to image stippling by extending identical disk packing to unequal disks. Experimental results demonstrate the efficacy of our approaches.
