Qian Qianqian, Cheng Ke, Qian Wei, Deng Qingchang, Wang Yuanquan
School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Sensors (Basel). 2022 Jun 30;22(13):4956. doi: 10.3390/s22134956.
The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.
梯度向量流（GVF）模型在计算机图像分割领域中得到了广泛应用。为了在图像处理中取得更好的效果，有许多基于GVF模型的研究论文。然而，很少有模型考虑图像结构。本文将GVF模型的平滑约束公式重新表示为矩阵形式，并将由海森矩阵表示的图像结纳入GVF模型。通过这一过程的处理，相关的扩散偏微分方程具有各向异性。基于海森矩阵的GVF模型（HBGVF）相对于其他相关的GVF方法具有许多优势，如能精确收敛到各种凹面、具有出色的弱边缘保留能力等。以下将通过理论分析和各种对比实验来证明我们所提出模型的优势。
