Whole pixel registration of non-rigid images with high accuracy and efficiency is a challenging problem in computer vision. To address this issue, we propose a correspondence vector field (CVF) Interpolation approach based on sparse matching of feature seeds. First, we detect and match two types of feature seeds to improve the accuracy of the later dense CVF interpolation. The first type of feature seeds is to guarantee the accuracy of the motion boundary, while the second one is to achieve the uniform distribution of seeds, which is helpful to improve the effect of interpolation. Second, we regionally estimate the dense CVF using the proposed interpolation approach on this basis. At last, we realize the whole-pixel registration of non-rigid images to yield the image alignment. Different from the traditional CVF interpolation approaches based on optical flow field, ours is based on the sparse matching of feature seeds. Thus, it is not limited to the large displacements and tends to achieve the accurate matching of certain key points easily, which is critical to the final interpolation result. Qualitative and quantitative experimental results on several internationally used datasets demonstrate that our approach outperforms the state-of-the-art ones.

非刚性图像的高精度和高效率的全像素配准是计算机视觉中一个具有挑战性的问题。为了解决这个问题，我们提出了一种基于特征种子的稀疏匹配的对应矢量场（CVF）插值方法。首先，我们检测并匹配两种类型的特征种子，以提高后期密集CVF插值的准确性。第一种特征种子是为了保证运动边界的准确性，而第二种特征种子是实现种子的均匀分布，这有助于提高插值效果。其次，我们在此基础上使用拟议的插值方法对密集CVF进行区域估计。最后，我们实现了非刚性图像的全像素配准，以产生图像对齐。与基于光流场的传统CVF插值方法不同，我们的方法基于特征种子的稀疏匹配。因此，它不限于大的位移，并且易于容易地实现某些关键点的精确匹配，这对于最终的内插结果至关重要。在多个国际使用的数据集上进行的定性和定量实验结果表明，我们的方法优于最新方法。