SURF: Speeded up robust features
SURF: Speeded up robust features
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DOI：
10.1007/11744023_32
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发表时间：
2006-01-01
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通讯作者：
Van Gool, Luc
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文献类型：
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作者：
Bay, Herbert;Tuytelaars, Tinne;Van Gool, Luc
在本文中，我们提出了一种新的尺度和旋转不变的兴趣点检测器和描述符，创造SURF（加速鲁棒特征）。它在可重复性、独特性和鲁棒性方面接近甚至优于先前提出的方案，但可以更快地计算和比较。这是通过依赖于积分图像进行图像卷积来实现的;通过建立在领先的现有检测器和描述符的优势之上（在这种情况下，使用用于检测器的基于Hessian矩阵的测量和基于分布的描述符）;以及通过将这些方法简化到本质。这导致新颖的检测、描述和匹配步骤的组合。本文提出了一个标准的评价集，以及在现实生活中的对象识别应用程序的背景下获得的图像上的实验结果。两者都显示了SURF的强劲表现。
In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.