Cong Yizhi, Wang Yan, Hou Wenju, Pang Wei
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, School of Artificial Intelligence, Jilin University, Changchun 130012, China.
College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Entropy (Basel). 2023 Jan 4;25(1):106. doi: 10.3390/e25010106.
Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. However, it is difficult to obtain sufficient correct matching points in low-textured or repetitively-textured regions, resulting in insufficient matching points in the overlapping region, and this further leads to the warping model being estimated erroneously. In this paper, we propose a novel and flexible approach by increasing feature correspondences and optimizing hybrid terms. It can obtain sufficient correct feature correspondences in the overlapping region with low-textured or repetitively-textured areas to eliminate misalignment. When a weak texture and large parallax coexist in the overlapping region, the alignment and distortion often restrict each other and are difficult to balance. Accurate alignment is often accompanied by projection distortion and perspective distortion. Regarding this, we propose hybrid terms optimization warp, which combines global similarity transformations on the basis of initial global homography and estimates the optimal warping by adjusting various term parameters. By doing this, we can mitigate projection distortion and perspective distortion, while effectively balancing alignment and distortion. The experimental results demonstrate that the proposed method outperforms the state-of-the-art in accurate alignment on images with low-textured areas in the overlapping region, and the stitching results have less perspective and projection distortion.
特征检测与正确匹配是图像拼接过程的基础。匹配是否正确以及匹配数量直接影响最终拼接结果的质量。目前，几乎所有的图像拼接方法都采用SIFT+RANSAC模式来提取和匹配特征点。然而，在低纹理或重复纹理区域难以获得足够的正确匹配点，导致重叠区域的匹配点不足，进而导致扭曲模型估计错误。在本文中，我们提出了一种新颖且灵活的方法，通过增加特征对应关系和优化混合项来实现。它能够在低纹理或重复纹理区域的重叠区域中获得足够的正确特征对应关系，以消除错位。当重叠区域中同时存在弱纹理和大视差时，对齐和扭曲往往相互制约，难以平衡。精确对齐往往伴随着投影扭曲和透视扭曲。针对这一问题，我们提出了混合项优化扭曲方法，该方法在初始全局单应性的基础上结合全局相似变换，并通过调整各项参数来估计最优扭曲。通过这样做，我们可以减轻投影扭曲和透视扭曲，同时有效地平衡对齐和扭曲。实验结果表明，所提出的方法在重叠区域具有低纹理区域的图像的精确对齐方面优于现有技术，并且拼接结果具有较少的透视和投影扭曲。
