如何使用Mikolajczyk的特征检测器/描述符的评估框架？ [英] How to use Mikolajczyk's evaluation framework for feature detectors/descriptors?

问题描述

我尝试用事实标准评估我的SURF描述符实现的正确性框架由Mikolajczyk等人al 。我使用OpenCV来检测和描述SURF特征，并使用相同的特征位置作为我的描述符实现的输入。

I'm trying the assess the correctness of my SURF descriptor implementation with the de facto standard framework by Mikolajczyk et. al. I'm using OpenCV to detect and describe SURF features, and use the same feature positions as input to my descriptor implementation.

为了评估描述符的性能，框架需要评估检测器重复性。不幸的是，重复性测试期望特征位置的列表以及限定围绕每个特征的图像区域的大小和取向的椭圆参数。但是，OpenCV的SURF检测器只提供特征位置，比例和方向。

To evaluate descriptor performance, the framework requires to evaluate detector repeatability first. Unfortunately, the repeatability test expects a list of feature positions along with ellipse parameters defining the size and orientation of an image region around each feature. However, OpenCV's SURF detector only provides feature position, scale and orientation.

相关论文建议从第二矩矩阵的特征值迭代地计算那些椭圆参数。这是唯一的办法吗？据我所见，这将需要一些时尚的OpenCV。没有办法从特征列表和输入图像计算那些椭圆参数（例如在Matlab中）？

The related paper proposes to compute those ellipse parameters iteratively from the eigenvalues of the second moment matrix. Is this the only way? As far as I can see, this would require some fiddling with OpenCV. Is there no way to compute those ellipse parameters afterwards (e.g. in Matlab) from the feature list and the input image?

有人曾经使用过这个框架，可以帮助我有一些见解或指针？

Has anyone ever worked with this framework and could assist me with some insights or pointers?

推荐答案

您可以使用OpenCV的文件evaluation.cpp。是在目录中的OpenCV / modules / features2d / src。在这个文件中，你可以使用类EllipticKeyPoint，这个类有一个函数将KeyPoint转换为ElipticKeyPoint

You can use the file evaluation.cpp from OpenCV. Is in the directory OpenCV/modules/features2d/src. In this file you could use the class "EllipticKeyPoint", this class has one function to convert "KeyPoint" to "ElipticKeyPoint"

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