【摘要】 提出了一种基于图上正则化的图像分割方法。将离散的正则化扩散框架应用到全监督的图像分割领域中;使用非下采样轮廓波变换提取图像的多方向多尺度几何特征,结合HSI分解产生的图像颜色特征,使用高斯核函数公式构造图中各顶点特征之间的权重,并使用以8连接为基础,跨度为2k,k=0,1,2,3的拓扑结构构造图,进而将这些特征统一到离散的正则化框架中,并将其应用于全监督彩色图像分割领域。实验结果证明:与基于图谱理论的Random Walker和LazySnapping图像分割方法相比,本方法具有抗噪声能力强,对边缘细节保留完整,对具有纹理不一致的图像区域分割能力强的优点。更多还原

【Abstract】 A new digital image segmentation method based on regularization on graphs is proposed,which applied a regularized diffusion framework to solve the image segmentation problem with supervised learning.The weight of the graph is generated by using Gaussian Kernel Function,combining with the geometric feature extracted from the image with contourlet transform and the color feature with HSI decomposition.The graph topology structure is an improved 8-connection topology whose step is 2k,k=0,1,2,3.Experimental results show that,compared with some graph spectral theory based image segmentation algorithms,such as Random Walker and the Lazy Snapping,the proposed method is robust for noisy pictures,which can reserve more complete boundary and have better performance on the section with inconsistent texture.更多还原