Research Institute of Intelligent Control andSystems, Harbin Institute of Technology, Harbin, China.
IEEE Trans Image Process. 2013 Oct;22(10):4086-95. doi: 10.1109/TIP.2013.2270110. Epub 2013 Jun 19.
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.
在本文中，我们描述了一种用于低景深（DOF）图像无监督分割的新算法。首先，使用多尺度重模糊模型在显著度空间中检测感兴趣的目标（OOI）。然后，为了确定 OOI 的边界，提出了一种基于混合能量函数的主动轮廓模型。在该模型中，采用了一个与显著图相关的全局能量项来寻找全局最小值，以及一个针对低 DOF 图像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来平衡全局和局部能量的权重。此外，还设计了一种无监督的曲线初始化方法来减少演化迭代次数。最后，我们在各种低 DOF 图像上进行了实验，结果表明该方法具有较高的鲁棒性和精度。
