School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China.
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China.
PLoS One. 2019 Jan 7;14(1):e0210354. doi: 10.1371/journal.pone.0210354. eCollection 2019.
The traditional image mosaic result based on SIFT feature points extraction, to some extent, has distortion errors: the larger the input image set, the greater the spliced panoramic distortion. To achieve the goal of creating a high-quality panorama, a new and improved algorithm based on the A-KAZE feature is proposed in this paper. This includes changing the way reference image are selected and putting forward a method for selecting a reference image based on the binary tree model, which takes the input image set as the leaf node set of a binary tree and uses the bottom-up approach to construct a complete binary tree. The root node image of the binary tree is the ultimate panorama obtained by stitching. Compared with the traditional way, the novel method improves the accuracy of feature points detection and enhances the stitching quality of the panorama. Additionally, the improved method proposes an automatic image straightening model to rectify the panorama, which further improves the panoramic distortion. The experimental results show that the proposed method cannot only enhance the efficiency of image stitching processing, but also reduce the panoramic distortion errors and obtain a better quality panoramic result.
基于 SIFT 特征点提取的传统图像拼接结果，在一定程度上存在拼接全景的变形误差：输入图像集越大，拼接全景的变形越大。为了达到创建高质量全景图的目标，本文提出了一种基于 A-KAZE 特征的新的改进算法。该算法包括改变参考图像的选择方式，并提出了一种基于二叉树模型选择参考图像的方法，该方法将输入图像集作为二叉树的叶节点集，并使用自底向上的方法构建完整的二叉树。二叉树的根节点图像是拼接得到的最终全景图像。与传统方法相比，该新方法提高了特征点检测的准确性，并增强了全景图的拼接质量。此外，改进后的方法提出了一种自动图像校正模型来校正全景图，进一步减小了全景图的变形误差，获得了更好的全景图质量。实验结果表明，所提出的方法不仅可以提高图像拼接处理的效率，还可以减小全景图的变形误差，获得更好的全景图质量。
