结合图割算法,提出了一种针对低景深(Depth of field,DOF)图像的自动分割模型.首先,通过改进的点锐度算法得到图像的点锐度图,并结合图像的颜色特征,得到一个四维的特征向量.其次,通过对图像点锐度图强边缘的计算,利用图像清晰部分边缘...结合图割算法,提出了一种针对低景深(Depth of field,DOF)图像的自动分割模型.首先,通过改进的点锐度算法得到图像的点锐度图,并结合图像的颜色特征,得到一个四维的特征向量.其次,通过对图像点锐度图强边缘的计算,利用图像清晰部分边缘较连续,模糊部分边缘较弱、连续性较差的特点得到图像初步的前景/背景区域.然后,对前景/背景的颜色和点锐度特征进行高斯混合模型(Gaussian mixture model,GMM)建模,结合全局、局部自适应的λ值,对图割算法的Shrinking bias现象进行改善.最后,通过迭代的图割算法对前景/背景区域进行修正.实验结果表明,该模型鲁棒性较高,分割结果更加精确.展开更多
Let Sα*be the familiar class of normalized starlike functions of order α in the unit disk. In this paper, we establish the Fekete and Szeg? inequality for the class Sα*, and then we generalize this result to the un...Let Sα*be the familiar class of normalized starlike functions of order α in the unit disk. In this paper, we establish the Fekete and Szeg? inequality for the class Sα*, and then we generalize this result to the unit ball in a complex Banach space or on the unit polydisk in Cn.展开更多
基金Supported by NNSF of China(Grant Nos.11561030,11471111 and 11261022)the Jiangxi Provincial Natural Science Foundation of China(Grant Nos.20152ACB20002 and 20161BAB201019)Natural Science Foundation of Department of Education of Jiangxi Province,China(Grant No.GJJ150301)
文摘Let Sα*be the familiar class of normalized starlike functions of order α in the unit disk. In this paper, we establish the Fekete and Szeg? inequality for the class Sα*, and then we generalize this result to the unit ball in a complex Banach space or on the unit polydisk in Cn.
