Generating Pointillism Paintings Using Multi-Class Blue Noise Sampling Based on Seurat's Color Composition

• Main Authors: Yi-Chian Wu, 吳宜倩
• Other Authors: Wen-Chieh Lin
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/67910894761664349305

碩士 === 國立交通大學 === 多媒體工程研究所 === 100 === In this thesis, we propose a new stippling technique, using a simple and intuitive concept to convert a color image into a pointillism painting. First, we collect, analyze, and imitate the color composition structure from Seurat‘s paintings. We further infer more color compositions, which do not contain in the reference painting, and include them in our color statistical model. Then, we use the modified multi-class blue noise sampling to distribute color points by looking up the color statistical model to imitate Seurat’s color composition. The blue noise property ensures that the color points are randomly located but remain spatially uniform. In our experiments, we use the multivariate goodness-of-fit tests to analyze our and other previous research’s results, comparing the color composition of each segmentation region to Seurat’s, and confirming that the color compositions of our results are most similar to Seurat’s painting habit.