Research of automatic and quick stitching algorithm of mine monitoring image
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摘要: 针对煤矿井下环境的复杂性及图像拼接算法本身的局限性,提出了一种矿井监控图像自动快速拼接算法。该算法在结合Harris算法和SIFT算法优点的基础上,利用改进的RANSAC算法对提取出来的特征点进行提纯匹配以及模型参数估计,使算法抗尺度变化能力和抗噪性能得到很大改善,同时采用位置敏感散列算法,提高了图像拼接的成功率与实时性。实验结果表明,该算法具有很好的鲁棒性和快速拼接能力,能够应用于矿井监控图像的自动拼接。Abstract: In view of complexity of underground environment of coal mine and limitations of image stitching algorithms, an automatic and quick stitching algorithm of mine monitoring image was proposed. The algorithm combines advantages of Harris algorithm and SIFT algorithm, and uses improved RANSAC algorithm for purification and matching of extracted feature points and model parameter estimation, so that the anti-scaling performance and noise immunity are greatly improved. It uses locality-sensitive hashing algorithm to improve success rate and real-time performance of image stitching. The experimental results show that the algorithm is robust and has fast stitching capability, and can be applied to image automatic stitching of mine monitoring image.
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Key words:
- coal mine /
- image stitching /
- image matching /
- Harris algorithm /
- SIFT algorithm /
- RANSAC algorithm
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