摘要
Chan等人提出的向量CV模型尽管解决了传统CV模型无法分割向量值图像的问题,但是向量CV模型对于含有噪声或遮挡物等复杂的图像,无法正确分割目标。针对此问题提出一种融合形状先验向量CV模型。其能量泛函主要包含形状先验项、图像区域信息项以及距离正则项。此能量函数使得主动轮廓和形状先验位置相近时停止演化。该模型所用形状模板可以与目标形状仿射不同,使得算法更加灵活。该模型对含噪以及目标遮挡的图像具有很好的分割效果。
Chan et al proposes the vector CV model to solve the problem that the traditional CV model cannot segment the vector images, but it has a bad effect on the complex images that have noise or occlusions, so this paper proposes the vector CV model combining shape prior. Its energy function is mainly composed of shape prior information term and image area information term and distance regularization term. When the evolved active contour and shape prior have similar posi-tions, the contour stops evolution. According to the affine transformation of shape, using a gradient descent algorithm for template to match makes the algorithm more flexible. The model has good segmentation result for the noise and clutter image.
出处
《计算机工程与应用》
CSCD
2014年第15期140-144,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.40671133
No.41171338)
