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首页> 外文期刊>International journal of computational vision and robotics >Stitching algorithms for biological specimen images
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Stitching algorithms for biological specimen images

机译:生物标本图像的拼接算法

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摘要

In this paper, we address the problem of combining multiple overlapping image sections of biological specimens to obtain a single image containing the entire specimen. This is useful in the digitisation of a large number of biological specimens stored in museum collections and laboratories. In the case of many large specimens, it means that the specimen must be captured in overlapping sections instead of a single image. In this research, we have compared the performance of several known algorithms for this problem. In addition, we have developed several new algorithms based on matching the geometry (width, slope, and curvature) of the specimens at the boundaries. Finally, we compare the performance of a bagging approach that combines the results from multiple stitching algorithms. Our detailed evaluation shows that brightness-based and curvature-based approaches produce the best matches for the images in this domain.
机译:在本文中,我们解决了将生物样本的多个重叠图像部分合并以获得包含整个样本的单个图像的问题。这对于博物馆藏品和实验室中存储的大量生物标本的数字化非常有用。对于许多大型标本,这意味着必须以重叠的部分而不是单个图像来捕获标本。在这项研究中,我们比较了针对此问题的几种已知算法的性能。此外,我们基于匹配样本边界处的几何形状(宽度,坡度和曲率),开发了几种新算法。最后,我们比较了结合多种缝合算法结果的装袋方法的性能。我们的详细评估表明,基于亮度和基于曲率的方法可以为该域中的图像提供最佳匹配。

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