Department of Engineering, Cambridge University, Cambridge, UK.
IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):105-19. doi: 10.1109/TPAMI.2008.275.
The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.
角点探测器的可重复性和效率决定了它在实际应用中是否有用。可重复性很重要，因为从不同位置观察相同的场景应该产生与相同的真实世界 3D 位置相对应的特征。效率很重要，因为这决定了探测器与进一步的处理相结合是否可以在帧率下运行。本文介绍了三项进展。首先，我们提出了一种新的特征检测启发式方法，并使用机器学习从该方法中推导出一种特征检测器，它可以使用不到可用处理时间的 5%来完全处理实时 PAL 视频。相比之下，大多数其他检测器甚至无法在帧率下运行（Harris 检测器 115%，SIFT 195%）。其次，我们对检测器进行了泛化，允许对其进行可重复性优化，而效率损失很小。第三，我们根据上述基于 3D 场景的可重复性标准对角点检测器进行了严格的比较。我们表明，尽管主要是为了速度而构建，但是在这些严格的测试中，我们的启发式检测器明显优于现有的特征检测器。最后，比较表明，使用机器学习可以显著提高可重复性，从而产生一种既非常快速又非常高质量的检测器。
