Nonparametric Statistical Active Contour Based on Inclusion Degree of Fuzzy Sets
Nonparametric Statistical Active Contour Based on Inclusion Degree of Fuzzy Sets
复制标题
基于模糊集包含度的非参数统计主动轮廓
DOI：
10.1109/tfuzz.2015.2505328
复制
发表时间：
2016-10-01
影响因子：
11.9
通讯作者：
Wang, Bin
中科院分区：
文献类型：
--
作者：
Gong, Maoguo;Li, Hao;Wang, Bin
将模糊集的包含度引入到图像分割中。图像分割问题被新颖地建模为在区域边界总长度的约束下，最小化内外区域之间的重叠率。考虑到模糊集和统计图像域的相似性质，我们用模糊隶属度函数来表示内外区域，并用非参数密度估计来估计它们。然后，采用模糊集的包含度来表示区域内外的重叠率。我们通过推导相关的梯度流和应用曲线进化技术来解决基于包含度的优化问题。在合成图像和真实图像上的实验结果证实了该方法的有效性。与以往用于解决相同的非参数统计分割问题的活动轮廓模型相比，我们的方法在效率和进化时间上都有很好的表现。
In this paper, inclusion degree of fuzzy sets is introduced to image segmentation. The image segmentation problem is novelly modeled as the minimization of the overlapping rates between the inside and outside regions, subject to a constraint on the total length of the region boundaries. Considering the similar properties of fuzzy sets and statistical image domain, we use fuzzy membership functions to represent the inside and outside regions and utilize nonparametric density estimates to estimate them. Then, the inclusion degree of fuzzy sets is adopted to formulate the overlapping rates between the inside and outside regions. We solve the inclusion-degree-based optimization problem by deriving the associated gradient flow and applying curve evolution techniques. Experimental results on both synthetic and real images confirm the effectiveness of the proposed method. Compared with the previous active contour models formulated to solve the same nonparametric statistical segmentation problem, our method performs well in efficiency and evolution time.