基于Mean Shift和FDoG的图像卡通化渲染

针对现有图像卡通化渲染算法区域划分不明显或提取的边界不够连贯的问题,提出了一种基于Mean Shift和FDoG的图像卡通化渲染方法。该方法通过区域分割与边界处理相融合的手段,获取区域明晰、边界光滑连贯的卡通对象,同时结合亮度量化策略等后处理技术净化对象,最终获得具有卡通效果的图像。渲染算法采用Mean Shift技术对图像进行分割,通过设置合适的参数获取若干有意义的区域;引入FDoG算法思想对图像边界进行分析和提取;最后去除或合并视觉上的干扰区域,并参考卡通画的亮度分布特点对图像进行亮度量化,得到最终的卡通风格图像。方法实现简单,自动化程度较高,实验结果较理想。     

Coherence-enhancing line drawing for color images

Line drawing plays an important role in many image-based non-photorealistic applications. However, most existing approaches use a grayscale edge detector for line extraction, so that only luminance differences between nearby image pixels is taken into account, but the chrominance differences is ignored. This leads to the undesirable consequence that visually significant edges in adjacent regions with different colors of similar luminance cannot be detected. To address this limitation, we present a novel enhanced line drawing method based on a flow-based difference-of-Gaussians （FDoG） filter. Because of an inherent property of the thresholded DoG edge model, captured lines may appear dislodged from the true edges in the image. To this end, we provide a gradient-guided warping technique so that smooth and coherent lines can be extracted in the correct location. The GPU implementation of the proposed algorithms allows real-time performance, and experimental examples with various color images demonstrate the method＇s superior qualitative performance over previous approaches.