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A new modified panoramic UAV image stitching model based on the GA-SIFT and adaptive threshold method

机译：一种基于GA-SIFT和自适应阈值方法的新修改的全景UAV图像拼接模型

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摘要

This paper presents panoramic unmanned aerial vehicle (UAV) image stitching techniques based on an optimal Scale Invariant Feature Transform (SIFT) method. The image stitching representation associates a transformation matrix with each input image. In this study, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between the images. An improved Geometric Algebra (GA-SIFT) algorithm is proposed to realize fast feature extraction and feature matching work for the scanned images. The proposed GA-SIFT method can locate more feature points with greater accurately than the traditional SIFT method. The adaptive threshold value method proposed solves the limitation problem of high computation load and high cost of stitching time by greater feature points extraction and stitching work. The modified random sample consensus method is proposed to estimate the image transformation parameters and to determine the solution with the best consensus for the data. The experimental results demonstrate that the proposed image stitching method greatly increases the speed of the image alignment process and produces a satisfactory image stitching result. The proposed image stitching model for aerial images has good distinctiveness and robustness, and can save considerable time for large UAV image stitching.
机译：本文介绍了基于最佳尺度不变特征变换（SIFT）方法的全景无人机（UAV）图像拼接技术。图像拼接表示将变换矩阵与每个输入图像相关联。在这项研究中，我们将缝合作为多图像匹配问题，并使用不变的本地功能在图像之间找到匹配。提出了一种改进的几何代数（GA-SIFT）算法来实现扫描图像的快速特征提取和特征匹配工作。所提出的GA-SIFT方法可以比传统的SIFT方法精确地定位更多特征点。自适应阈值方法通过更大的特征点提取和拼接工作解决了高计算负荷和拼接时间的高成本的限制问题。提出了修改的随机样本共识方法来估计图像变换参数，并以最佳的数据共识确定解决方案。实验结果表明，所提出的图像拼接方法大大提高了图像对准过程的速度，并产生令人满意的图像拼接结果。所提出的空中图像图像拼接模型具有良好的独特性和鲁棒性，可以节省大的UAV图像拼接的相当长的时间。

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