如何将 OpenCV 特征描述符标准化为整数尺度？
OpenCV SURF 实现为图像中找到的每个特征点返回 64/128 32 位浮点值（描述符）的序列。有没有办法标准化这个浮点值并将它们转换为整数范围（例如，[0, 255]）？这将节省重要的空间（每个值 1 或 2 个字节，而不是 4 个字节）。此外，转换应确保描述符对其他用途（例如聚类）仍然有意义。
谢谢！
OpenCV SURF implementation returns a sequence of 64/128 32 bit float values (descriptor) for each feature point found in the image. Is there a way to normalize this float values and take them to an integer scale (for example, [0, 255])?. That would save important space (1 or 2 bytes per value, instead of 4). Besides, the conversion should ensure that the descriptors remain meaningful for other uses, such as clustering.
Thanks!
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除了 SURF 之外还有其他特征提取器。 Brief 提取器每个描述符仅使用 32 个字节。它使用 32 个无符号字节 [0-255] 作为其元素。您可以像这样创建一个：
Ptr ptrExtractor = DescriptorExtractor::create("BRIEF");请注意，OpenCV 中的许多图像处理例程需要或假设数据存储为浮点数。
There are other feature extractors than SURF. The BRIEF extractor uses only 32 bytes per descriptor. It uses 32 unsigned bytes [0-255] as its elements. You can create one like this:
Ptr ptrExtractor = DescriptorExtractor::create("BRIEF");Be aware that a lot of image processing routines in OpenCV need or assume that the data is stored as floating-point numbers.
您可以将浮动特征视为普通图像（Mat 或 cvmat），然后使用 cv::normalize()。另一种选择是使用 cv::norm() 查找描述符值的范围，然后使用 cv::convertTo() 转换为 CV_8U。查找这些函数的 OpenCV 文档。
You can treat the float features as an ordinary image (Mat or cvmat) and then use cv::normalize(). Another option is using cv::norm() to find the range of descriptor values and then cv::convertTo() to convert to CV_8U. Look up the OpenCV documentation for these functions.
cv::SurfFeatureDetector 返回的描述符已经标准化。您可以通过返回的 cv::Mat 的 L2 Norm 来验证这一点，或者参考论文。
The descriptor returned by cv::SurfFeatureDetector is already normalized. You can verify this by taking the L2 Norm of the cv::Mat returned, or refer to the paper.