Computer Vision – ECCV 2006
Computer Vision – ECCV 2006
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DOI：
10.1007/11744023
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发表时间：
2006
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在实时帧率应用中使用特征点的情况下，需要高速特征检测器。特征检测器，如SIFT（DOG），哈里斯和SUSAN是很好的方法，产生高质量的功能，但他们太计算密集的实时应用程序中使用的任何复杂性。在这里，我们展示了机器学习可以用来导出一个特征检测器，它可以使用不到7%的可用处理时间来完全处理实时PAL视频。相比之下，Harris检测器（120%）和SIFT的检测级（300%）都不能以全帧速率操作。显然，如果产生的特征不适合下游处理，则高速检测器的用途有限。特别是，从两个不同位置观看的同一场景应该产生对应于相同真实世界3D位置的特征[1]。因此，本文的第二个贡献是比较角点检测器应用于3D场景的基础上，这个标准。这种比较支持了其他地方提出的关于现有角检测器的一些主张。此外，与我们最初的预期相反，我们表明，尽管主要是为了速度而构建的，但根据这一标准，我们的检测器的性能显着优于现有的特征检测器。
Where feature points are used in real-time frame-rate applications, a high-speed feature detector is necessary. Feature detectors such as SIFT (DoG), Harris and SUSAN are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Here we show that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time. By comparison neither the Harris detector (120%) nor the detection stage of SIFT (300%) can operate at full frame rate.Clearly a high-speed detector is of limited use if the features produced are unsuitable for downstream processing. In particular, the same scene viewed from two different positions should yield features which correspond to the same real-world 3D locations [1]. Hence the second contribution of this paper is a comparison corner detectors based on this criterion applied to 3D scenes. This comparison supports a number of claims made elsewhere concerning existing corner detectors. Further, contrary to our initial expectations, we show that despite being principally constructed for speed, our detector significantly outperforms existing feature detectors according to this criterion.