A Curve Evolution Approach for Unsupervised Segmentation of Images With Low Depth of Field
A Curve Evolution Approach for Unsupervised Segmentation of Images With Low Depth of Field
复制标题
DOI：
10.1109/tip.2013.2270110
复制
发表时间：
2013-10
影响因子：
10.6
通讯作者：
Jiangyuan Mei;Yulin Si;Huijun Gao
中科院分区：
文献类型：
--
作者：
Jiangyuan Mei;Yulin Si;Huijun Gao
在本文中，我们描述了一种新的无监督分割算法的图像与低景深（DOF）。首先，利用多尺度重模糊模型在显著性空间中检测出感兴趣目标。然后，提出了一种基于混合能量函数的活动轮廓模型，用于确定OOI的边界。该模型采用与显著图相关的全局能量项来寻找全局最小值，并采用与低自由度图像相关的局部能量项来提高分割精度。此外，该模型还附加了一个自适应参数，以平衡全局和局部能量的权重。此外，设计了一种无监督的曲线初始化方法，以减少进化迭代次数。最后，我们对各种低自由度图像进行了实验，结果表明，该方法具有较高的鲁棒性和精度。
In this paper, we describe a novel algorithm for unsupervised segmentation of images with low depth of field (DOF). First of all, a multi-scale reblurring model is used to detect the object of interest (OOI) in saliency space. Then, to determine the boundary of OOI, an active contour model based on hybrid energy function is proposed. In this model, a global energy item related with the saliency map is adopted to find the global minimum, and a local energy term regarding the low DOF image is used to improve the segmentation precision. In addition, an adaptive parameter is attached to this model to balance the weight of global and local energy. Furthermore, an unsupervised curve initialization method is designed to reduce the number of evolution iterations. Finally, we conduct experiments on various low DOF images, and the results demonstrate the high robustness and precision of the proposed approach.