CAJ | 학술논문

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。
Chan등인제출적향량CV모형진관해결료전통CV모형무법분할향량치도상적문제，단시향량CV모형대우함유조성혹차당물등복잡적도상，무법정학분할목표。침대차문제제출일충융합형상선험향량CV모형。기능량범함주요포함형상선험항、도상구역신식항이급거리정칙항。차능량함수사득주동륜곽화형상선험위치상근시정지연화。해모형소용형상모판가이여목표형상방사불동，사득산법경가령활。해모형대함조이급목표차당적도상구유흔호적분할효과。
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.