基于冲击滤波器的视频图像卡通化算法
|
摘要
传统二维卡通绘制的专业性和复杂性导致普通用户参与性较低。为此,提出一种基于冲击滤波器的视频图像卡通化算法。应用改进的冲击滤波器对彩色视频图像进行颜色聚类和噪声消除,对冲击滤波后的图像使用高斯差分算子进行边缘检测,并做颜色量化处理,融合边缘曲线和量化图像生成个性化的卡通图像。实验结果表明,与基于双边滤波器、Osher冲击滤波器、Alvarez冲击滤波器以及改进Osher冲击滤波器的图像卡通化算法相比,该算法能够获得更清晰、完整、光滑和连续的边缘曲线,风格化图像的视觉显著性更强,保真度更高,并能通过系列图像的风格化转换自动生成个性化的卡通视频。
A video image cartoonlization algorithm based on shock filter is proposed for the problem that the specialty and complexity of the traditional two dimensional cartoon production lead to the low participation of users. The improved shock filter is to cluster the color and eliminate the noise of the color video image, and the Gaussians difference operator is used to detect edges of the image which is filtered by shock filter. Then, color quantization is applied to the image which has been filtered by shock filter. The edge curves and the quantitative image are fused to generate a personalized cartoon image. Experimental results show that compared with the algorithm for cartoon like stylization of image based on the bilateral filter, shock filter proposed by Osher, shock filter proposed by Alvarez and improvement of the shock filter proposed by Osher, this algorithm can produce stronger visual distinctiveness and higher fidelity of cartoon image with more clearly, more complete, more smooth and more continuous edge curves, and it can generate personalized cartoon video automatically through the stylization conversion of a series of images.
A video image cartoonlization algorithm based on shock filter is proposed for the problem that the specialty and complexity of the traditional two dimensional cartoon production lead to the low participation of users. The improved shock filter is to cluster the color and eliminate the noise of the color video image, and the Gaussians difference operator is used to detect edges of the image which is filtered by shock filter. Then, color quantization is applied to the image which has been filtered by shock filter. The edge curves and the quantitative image are fused to generate a personalized cartoon image. Experimental results show that compared with the algorithm for cartoon like stylization of image based on the bilateral filter, shock filter proposed by Osher, shock filter proposed by Alvarez and improvement of the shock filter proposed by Osher, this algorithm can produce stronger visual distinctiveness and higher fidelity of cartoon image with more clearly, more complete, more smooth and more continuous edge curves, and it can generate personalized cartoon video automatically through the stylization conversion of a series of images.
引文
[1]周科颖.图像风格化技术研究及平台开发[D].杭州:浙江工业大学,2010.
[2]樊雅平,黄生学.基于Mean-shift和DoG的卡通化图像生成算法[J].煤炭技术,2009,28(9):160-161.
[3]Gooch B,Reinhard E,Gooch A.Human Facial Illustrations:Creation and Psychophysical Evaluation[J].ACM Transactions on Graphics,2004,23(1):27-44.
[4]Winnemoller H,Olsen S C,Gooch B.Real-time Video Abstraction[J].ACM Transactions on Graphics,2006,25(3):1221-1226.
[5]Osher S C,Rudin L I.Feature-oriented Image Enhancement Using Shock Filters[J].SIAM Journal on Numerical Analysis,1990,27(4):919-940.
[6]Comaniciu D,Meer P.Mean-shift:A Robust Approach Toward Feature Space Analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
[7]Tomasi C,Manduchi R.Bilateral Filtering for Gray and Color Images[C]//Proc.of IEEE International Conference on Computer Vision.Bombay,India:IEEE Press,1998:839-846.
[8]Alvarez L,Mazorra L.Signal and Image Restoration Using Shock Filters and Anisotropic Diffusion[J].SIAM Journal on Numerical Analysis,1994,31(2):590-605.
[9]Kramer H P,Bruckner J B.Iterations of a Non-linear Transformation for Enhancement of Digital Images[J].Pattern Recognition,1975,7(1):53-58.
[10]Brockett R W,Maragos P.Evolution Equations for Continuousscale Morphology[C]//Proc.of IEEE International Conference on Acoustics,Speech and Signal Processing.San Francisco,USA:IEEE Signal Processing Society,1992:125-128.
[11]Osher S,Rudin L I.Shocks and Other Nonlinear Filtering Applied to Image Processing[C]//Proceedings of SPIE Conference on Applications of Digital Image Processing XIV.Bellingham,USA:SPIE Press,1991:414-431.
[12]Marr D,Hildreth E C.Theory of Edge Detection[C]//Proc.of the Royal Society of London Biological Sciences.London,UK:Defense Technical Information Center,1980:187-217.
[13]Haralich R.Digital Step Edges from Zero Crossings of Second Directional Derivatives[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(1):58-68.
[14]付树军,阮秋琦,穆成坡,等.基于双向耦合扩散的保持特征的边缘锐化和图像增强[J].计算机学报,2008,31(3):529-535.
[2]樊雅平,黄生学.基于Mean-shift和DoG的卡通化图像生成算法[J].煤炭技术,2009,28(9):160-161.
[3]Gooch B,Reinhard E,Gooch A.Human Facial Illustrations:Creation and Psychophysical Evaluation[J].ACM Transactions on Graphics,2004,23(1):27-44.
[4]Winnemoller H,Olsen S C,Gooch B.Real-time Video Abstraction[J].ACM Transactions on Graphics,2006,25(3):1221-1226.
[5]Osher S C,Rudin L I.Feature-oriented Image Enhancement Using Shock Filters[J].SIAM Journal on Numerical Analysis,1990,27(4):919-940.
[6]Comaniciu D,Meer P.Mean-shift:A Robust Approach Toward Feature Space Analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
[7]Tomasi C,Manduchi R.Bilateral Filtering for Gray and Color Images[C]//Proc.of IEEE International Conference on Computer Vision.Bombay,India:IEEE Press,1998:839-846.
[8]Alvarez L,Mazorra L.Signal and Image Restoration Using Shock Filters and Anisotropic Diffusion[J].SIAM Journal on Numerical Analysis,1994,31(2):590-605.
[9]Kramer H P,Bruckner J B.Iterations of a Non-linear Transformation for Enhancement of Digital Images[J].Pattern Recognition,1975,7(1):53-58.
[10]Brockett R W,Maragos P.Evolution Equations for Continuousscale Morphology[C]//Proc.of IEEE International Conference on Acoustics,Speech and Signal Processing.San Francisco,USA:IEEE Signal Processing Society,1992:125-128.
[11]Osher S,Rudin L I.Shocks and Other Nonlinear Filtering Applied to Image Processing[C]//Proceedings of SPIE Conference on Applications of Digital Image Processing XIV.Bellingham,USA:SPIE Press,1991:414-431.
[12]Marr D,Hildreth E C.Theory of Edge Detection[C]//Proc.of the Royal Society of London Biological Sciences.London,UK:Defense Technical Information Center,1980:187-217.
[13]Haralich R.Digital Step Edges from Zero Crossings of Second Directional Derivatives[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(1):58-68.
[14]付树军,阮秋琦,穆成坡,等.基于双向耦合扩散的保持特征的边缘锐化和图像增强[J].计算机学报,2008,31(3):529-535.