中文摘要：

为了分割图像中的多个目标，提出多先验形状约束的多目标图割分割方法。首先，使用离散水平集框架的形状距离定义先验形状模型，并将这一模型合并到图割框架的区域项中，同时通过加入多类形状先验扩展形状先验能量。然后，通过自适应调节形状先验项的权重系数，实现自适应控制形状项在能量函数中所占的比重，克服人工选择参数的困难，提高分割效率。最后，为使方法对于形状仿射变换具有不变性，使用尺度不变特征变换和随机抽样一致结合的方法进行对准。实验表明，文中方法能够较好分割图像中的多个目标，且能较好克服图像的噪声污染、目标被遮挡等信息缺失问题。

英文摘要：

To segment multiple objects in the graph, a graph cut segmentation method for multiple priori shape constraints is proposed. The shape distance in a discrete level set framework is used to define the priori shape model ,and then this model is merged into the regional item of the graph cut framework. The priori energy function is expanded by adding multiple shape priors. The weight coefficient of shape prior item is adaptively adjusted to realize the adaptive control of shape items accounted for the proportion of the energy function. And thus, the problem of artificial selection of parameters is solved and the efficiency of segmentation is enhanced. To obtain the invariance of the method proposed in this research for shape＆amp;nbsp;affine transformation, the techniques combining the scale invariant feature transform and the random sample consensus are employed to align. The experimental results indicate that multiple targets in the image can be segmented by the proposed method. Moreover, the image noise pollution as well as occlusion is inhibited.