A Method of Using Genetic Algorithm in Image Stitching

L. Zhao, Yujie Huang, Ming-e Jing, Xiaoyang Zeng, Yibo Fan
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Abstract

Image stitching is an important part of computer vision, and how to do it more efficiently with high quality is a heated topic. In this paper, the authors propose a new method called TMGA for image stitching to get an improved performance in calculating Transform Matrix by using Genetic Algorithm. The proposed TMGA not only counts the number of interior points, but also takes standard error and degree of dispersion into consideration compared the traditional methods. The results demonstrate that the proposed algorithm can gain a high-quality transform matrix and improves the result of the stitching.
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遗传算法在图像拼接中的应用
图像拼接是计算机视觉的重要组成部分,如何高效、高质量地进行图像拼接一直是研究的热点。本文提出了一种新的图像拼接方法TMGA,提高了利用遗传算法计算变换矩阵的性能。与传统方法相比,所提出的TMGA不仅计算了内部点的个数,而且考虑了标准误差和离散度。结果表明,该算法可以获得高质量的变换矩阵,提高了拼接效果。
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