Image segmentation is a challenging problem in computer vision with wide application. It is a process which considers the similarity criterion required to separate an image into different homogenous connected regions. First, an Optimized Adaptive Connectivity and Shape Prior in Modified Graph Cut Segmentation method has been applied to handle the structural irregularities in images. Second, an Optimized Adaptive Connectivity and Shape Prior in Modified Fuzzy Graph Cut Segmentation (Opac‐MFGseg) is proposed to partition the images based on feature values. In this method, a fuzzy rule based system is used with optimization algorithm to provide the information on how much a specific feature is involved in image boundaries. The graph obtained from this fuzzy approach is further used in adaptive shape prior in modified graph cuts framework. Moreover, this method supports moving images (videos). In such a situation, a fully dynamic method called Optimized Adaptive Connectivity and Shape Prior in Dynamic Fuzzy Graph Cut Segmentation (Opac‐DFGseg) method is proposed for the image segmentation. The effectiveness of the Opac‐MFGseg and Opac‐DFGseg methods is tested in terms of average sensitivity, precision, area overlap measure, relative error, and accuracy and computation time.

中文翻译：

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基于自适应连通性和形状先验的模糊图切彩色图像分割研究

在广泛应用的计算机视觉中，图像分割是一个具有挑战性的问题。这是考虑将图像分离成不同的均匀连接区域所需的相似性标准的过程。首先，在修改后的图割分割中采用了优化的自适应连通性和形状先验的方法来处理图像中的结构不规则性。其次，提出了一种改进的自适应连接性和改进的模糊图割分割（Opac-MFGseg）形状先验算法，用于基于特征值的图像分割。在此方法中，基于模糊规则的系统与优化算法一起使用，以提供有关特定特征在图像边界中涉及多少的信息。从这种模糊方法获得的图形在修改后的图形切割框架中先用于自适应形状。此外，此方法支持运动图像（视频）。在这种情况下，提出了一种完全动态的方法，称为动态模糊图切割分割中的优化的自适应连通性和形状先验（Opac-DFGseg）方法，用于图像分割。Opac-MFGseg和Opac-DFGseg方法的有效性通过平均灵敏度，精度，面积重叠测量，相对误差以及准确性和计算时间进行了测试。