一种改进的水平集主动轮廓模型

摘要: 通过统计具有图像增强能力的局部区域信息,提出了一种改进的水平集主动轮廓模型.结合非负核函数和具有矫正图像误差特点的灰度集群思想,定义了一种新的符号压力函数(SPF),此函数能够很好处理灰度不均匀、对象边界不明确的问题. 并且,引入惩罚项, 确保水平集函数的适应性.真实图像和合成图像的实验结果表明, 该模型收敛迅速, 具有抗噪性,对分割目标图像敏感, 能够处理弱边界的多目标图像
- 一种改进的水平集主动轮廓模型 /
- 弱边界 /
- 灰度不均匀 /
- 图像分割
Abstract: By using the local regional information which has the ability to enhance the image, an improved active contour model based on level set method is proposed. Defining a novel SPF function with a nonnegative kernel function and local intensity clustering property, the novel model could draw upon intensity information in local regions at a controllable scale. In addition, the penalizing term which can be called distance regularization term drives the motion of the zero level set toward desired locations. Experimental results for real and synthetic images show the desirable proposed method performances and the model efficiency on intensity inhomogeneities and weak boundaries
- an improved active contour model based on level set method /
- weak boundary /
- intensity inhomogeneity /
- image segmentation

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