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Automatic image mosaic based on SIFT using bidirectional matching

机译:使用双向匹配基于SIFT的自动图像拼接

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In the paper, an image mosaic algorithm based on SIFT feature matching is proposed. For an image mosaic method based on feature matching, feature detection is needed to perform in each image. Thus a rapid detection operator is essential to the efficiency of the whole algorithm. In this paper, we use SIFT to extract features. The extracted features are matched by k-d tree and bidirectional matching strategy to enhance the accuracy of matching. Then, a RANSAC algorithm is applied to eliminate outliers to ensure effectiveness of the matching. Finally images are stitched by weighted average blending algorithm. The presented algorithm overcomes the disadvantages of the traditional image mosaic methods which are susceptible to different scale and moving objects, and can achieve sub-pixel accuracy of matching and algorithm is still available to the images at different scale. Experimental results show that the method with strong robustness performs effectively.
机译:提出了一种基于SIFT特征匹配的图像拼接算法。对于基于特征匹配的图像拼接方法,需要在每个图像中执行特征检测。因此,快速检测算子对于整个算法的效率至关重要。在本文中,我们使用SIFT提取特征。提取的特征通过k-d树和双向匹配策略进行匹配,以提高匹配的准确性。然后,应用RANSAC算法消除异常值,以确保匹配的有效性。最后,通过加权平均混合算法对图像进行拼接。提出的算法克服了传统图像拼接方法易受不同尺度和运动物体影响的缺点,可以达到亚像素匹配的精度,并且该算法仍然适用于不同尺度的图像。实验结果表明,该方法具有较强的鲁棒性。

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