OpenCV：何时使用GridAdaptedFeatureDetector？ [英] OpenCV: When to use GridAdaptedFeatureDetector?

问题描述

我想根据描述符做一个检测器。我使用OpenCV，我已经看到有很多功能类型和描述符类型，以及匹配器类型。更多我还看到，对于要素类型，可以有网格或金字塔的组合类型。

我没有找到一个很好的解释（除了金字塔，说这是好的不固有地缩放的检测器）。我想对每个类型和每个组合（特征描述符匹配器）有一个小描述，以提出一个想法，而不是创建一个详尽的工作搜索和验证每个可能的组合。

有没有人知道更多相关资讯？

解决方案

对于两种不同的情况：

• 特征 ，
。 有趣的点，即在视点和照明变化下是稳定的并且在诸如单应性估计或对象检测的任务中产生良好性能的点。




描述符旨在到达



某些点设计为单独，没有任何描述符。大多数最老的兴趣点（Moravec，Harris，好的功能）和最近的一小部分（FAST）都是这种情况。





然后，通过点检测器和描述符的共同设计实现了主要的性能提升，这是 SIFT和SURF 所包含的方法。
为了简单起见，描述符没有给出特定的名称（虽然你可以说SIFT描述符和HoG特征彼此非常接近）。
这些描述符是实值（即浮点向量）。





最后，为了快速运行次在有限的硬件上，设计了原始关键点检测器（FAST）。 FAST依赖于简单的二进制测试。
然后使用相同的二进制测试方法设计描述符，这就是你如何得到BRIEF，BRISK，FREAK，ORB ...
因此，你得到的是二进制描述符<





最后，如果您想总结：






• >您可以根据需要交叉描述符和检测器。请注意，当探测器没有刻度时，您可能必须为需要它的描述符（SIFT，SURF）猜出一个（或强加默认值）;


• 任何匹配器可以使用，只要你有从每个图像相同类型的描述符。匹配器使用的特征距离将有什么变化;


• SIFT和SURF是实际值，因此需要使用距离。最近的描述符（BRIEF，BRISK，FREAK，ORB）为二进制，而距离必须以 距离来衡量。





I am trying to make a detector based on descriptors. I am using OpenCV and I have seen that there are many feature types and descriptor types, and also matcher types. More I have also seen that there can be composed types like Grid or Pyramid for feature types.

I have not found a good explanation of them (except for Pyramid, where it says that is good "for detectors that are not inherently scaled"). I want to have a small description of each type and each combination (feature-descriptor-matcher), to make an idea and not creating an exhaustive work searching and verifying each possible combination.

Does anyone know some more information about this?

解决方案

The term feature is commonly used for two different things:

• feature detectors,
• feature descriptors.

A detector aims at.. well... detecting good interesting points, i.e., points that are stable under viewpoint and illumination changes and that yield good performance in tasks like homography estimation or object detection.

A descriptor aims at reaching good matching performance for detected points under the said viewpoint and illumination changes.

Some points were designed individually, without any descriptor. This is the case for most of the oldest interest points (Moravec, Harris, good features) and a small portion of the recent ones (FAST).

Then, a major performance improvement was reached through the co-design of point detectors and descriptors, and this is the approach embraced by SIFT and SURF. For simplicity, the descriptor was not given a particular name (although you can remark that SIFT descriptors and HoG features are very close to each other). These descriptors are real valued (i.e., floating point vectors).

Finally, in order to have fast running times on limited hardware, an original keypoint detector (FAST) was designed. FAST relies on simple binary tests. The same approach of binary tests was then used to design descriptors, and this is how you got BRIEF, BRISK, FREAK, ORB... Thus, what you get is binary descriptors (bitstreams).

Finally, if you want to summarize:

• you can cross descriptors and detectors as you like. Just be careful that when a detector does not have a scale you may have to guess one (or impose a default one) for the descriptors that require it (SIFT, SURF);
• any matcher can be used as long as you have the same type of descriptors from each image. What will vary is the feature distance used by the matcher;
• SIFT and SURF are real valued, thus need to be matched using an L2 distance. Recent descriptors (BRIEF, BRISK, FREAK, ORB) are binary and distances must be measured with the Hamming distance.

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