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Approximated Scale Space for Efficient and Accurate SIFT Key-Point Detection

机译：高效和准确的SIFT关键点检测的近似比例空间

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摘要

The SIFT (scale invariant feature transform) key-point serves as an indispensable role in many computer vision applications. This paper presents an approximation of the SIFT scale space for key-point detection with high efficiency while preserving the accuracy. We build the scale space by repeated averaging filters to approximate the Gaussian filters used in SIFT algorithm. The accuracy of the proposed method is guaranteed by that an image undergoes repeated smoothing with an averaging filter is approximately equivalent to the smoothing with a specified Gaussian filter, which can be proved by the center limit theorem. The efficiency is improved by using integral image to fast compute the averaging filtering. In addition, we also present a method to filter out unstable key-points on the edges. Experimental results demonstrate the proposed method can generate high repeatable key-points quite close to the SIFT with only about one tenth of computational complexity of SIFT, and concurrently the proposed method does outperform many other methods.
机译：SIFT（尺度不变特征变换）关键点在许多计算机视觉应用中起着不可或缺的作用。本文提出了一种SIFT尺度空间的近似值，可以在保持精度的同时高效地进行关键点检测。我们通过重复平均滤波器以逼近SIFT算法中使用的高斯滤波器来构建尺度空间。通过使用平均滤波器对图像进行重复的平滑处理近似等于使用指定高斯滤波器进行的平滑处理，可以保证所提出方法的准确性，这可以通过中心极限定理来证明。通过使用积分图像快速计算平均滤波，可以提高效率。此外，我们还提出了一种过滤掉边缘上不稳定的关键点的方法。实验结果表明，该方法可以生成非常接近SIFT的高可重复关键点，而计算复杂度仅为SIFT的十分之一，并且同时也优于其他许多方法。

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