特征检测和描述符提取之间的差异 [英] Difference between Feature Detection and Descriptor Extraction

问题描述

有没有人知道在OpenCV 2.3中FeatureDetection和DescriptorExtraction之间的区别？我理解后者是使用DescriptorMatcher进行匹配所必需的。如果是这样，FeatureDetection用于什么？

Does anyone know the difference between FeatureDetection and DescriptorExtraction in OpenCV 2.3? I understand that the latter is required for matching using DescriptorMatcher. If that's the case, what is FeatureDetection used for?

谢谢。

推荐答案

功能检测

Feature detection

• 在计算机视觉和图像处理中，涉及旨在计算图像信息的抽象并在每个图像点进行局部决定的方法，无论在该点是否存在给定类型的图像特征。结果特征将是图像域的子集，通常以孤立点，连续曲线或连接区域的形式。

• In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions.

特征检测= （例如：查找一个角，查找模板等）

Feature detection = how to find some interesting points (features) in the image.(Ex: find a corner, find a template and so on...)

功能提取

• 识别和在图像处理中，特征提取是一种特殊形式的降维。当到算法的输入数据太大而不能被处理并且被怀疑是众所周知的冗余（多数据，但是没有多少信息）时，输入数据将被变换为特征的简化表示集合（也称为特征向量） 。将输入数据变换成特征集合称为特征提取。如果仔细选择提取的特征，则期望特征集将从输入数据中提取相关信息，以便使用该缩减的表示而不是全尺寸输入来执行期望的任务。

• In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (much data, but not much information) then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input.

特征提取=如何表示我们发现的有趣点，以便与图像中的其他有趣的点（特征）进行比较。 （Ex，该点的局部区域强度​​→点周围区域的局部方向...等）

Feature extraction = how to represent the interesting points we found to compare them with other interesting points (features) in the image. (Ex, the local area intensity of this point? The local orientation of the area around the point? and so on...)

实例：你可以用哈里斯角法找到一个角落，但你可以用任何你想要的方法来描述它（直方图，HOG，

您可以在这里看到更多的信息，
维基百科链接

You can see here some more informations, Wikipedia link

Julien，（帮助维基百科：p）

Julien, (Helped with wikipedia :p)

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