基于局部图划分的多相活动轮廓图像分割模型

几何活动轮廓模型是图像分割领域的强有力工具。最近，一种基于成对相似性的图划分活动轮廓(GPAC)模型被提出，并有效应用于均质图像分割。但是，该模型的连接权函数仅与图像光谱相关，使得模型在低对比度模糊图像的应用存在较大局限，同时，成对相似性的计算量大，模型的数值实现效率不甚理想。针对这些问题，该文引入测地核函数定义连接权函数，结合多相水平集，提出了基于局部图划分的多相活动轮廓图像分割模型。自然图像的实验结果证明了该模型的有效性。

Localized Graph-cuts Based Multiphase Active Contour Model for Image Segmentation

Geometric active contour is used as a powerful tool to address image segmentation problems. Recently, a new region-based active contour model based on pairwise similarity between pixels, i.e., Graph Portioning Active Contours (GPAC) is presented, and well adapted to segment images with intensity homogeneity. However, it only takes spectral similarity as the cost function between vertices, and can not obtain satisfactory segmentation results for low contrast images with weak boundaries. In order to overcoming this limitation of GPAC model, a novel localized graph-cuts based multiphase active contours model using geodesic kernel based cost function is proposed. Experimental results of natural images verify that the model is efficient and accurate.