DMatches中的distance属性是什么意思? [英] What does the distance attribute in DMatches mean?

问题描述

我有一个简短的问题:当我在OpenCV中进行功能匹配时，MatOfMatches中DMatches的distance属性是什么意思?

I have a short question: When I do feature-matching in OpenCV, what does the distance attribute mean of DMatches in MatOfMatches?

我知道我必须过滤较大距离的匹配项，因为它们不如较小距离的匹配项好.但是此属性的含义是什么?是一种偏离吗?

I know that I have to filter matches with bigger distance because they aren't as good as them with lower distance. But what is the meaning of this attribute? Is it a kind of deviation?

推荐答案

在这种情况下，功能是图像上的关注点.为了比较功能，您可以使用功能检测器描述"它们.然后，每个功能都与一个描述符相关联.匹配特征时，实际上就是匹配它们的描述符.

In this context, a feature is a point of interest on the image. In order to compare features, you "describe" them using a feature detector. Each feature is then associated to a descriptor. When you match features, you actually match their descriptors.

描述符是多维向量.它可以是实值(例如 SIFT )或 binary (例如Brief).

A descriptor is a multidimensional vector. It can be real-valued (e.g. SIFT) or binary (e.g. BRIEF).

匹配是一对描述符，每个图像一个，在所有描述符中最相似.当然，要在图像B中找到与图像A中的描述符最相似的描述符，您需要对此相似程度进行度量.

A matching is a pair of descriptors, one from each image, which are the most similar among all of the descriptors. And of course, to find the descriptor in image B that is the most similar to a descriptor in image A, you need a measure of this similarity.

有两种方法可以计算两个向量之间的相似度".对于实值描述符，通常使用 欧几里德距离 ，当 汉明距离 对于二进制描述符来说很常见.

There are multiple ways to compute a "score of similarity" between two vectors. For real-valued descriptors, the Euclidean distance is often used, when the Hamming distance is common for binary descriptors.

作为结论，我们现在可以了解distance属性:它是匹配的两个描述符之间的相似性得分.

As a conclusion, we can now understand the distance attribute: it is the score of similarity between the two descriptors of a match.

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