minHessian (Surffeaturedetector) 的解释

原文地址：http://stackoverflow.com/questions/17613723/whats-the-meaning-of-minhessian-surffeaturedetector

原文内容：To decide if a pixel in an image is a SURF keypoint, an approximation of the Hessian matrix is built with the partial derivatives of the image intensities within a patch around the pixel. The determinant of this matrix is called Hessian and tells you how robust that pixel is as a blob center.

The minHessian is a threshold to decide from which value you are willing to accept keypoints. In practice, the higher the minHessian, the fewer keypoints you will obtain, but you expect them to be more repetitive (w.r.t. image transformations), and then, more useful. On the other hand, the lower the minHessian, the more keypoints you get, but they may be more noisy.

In usual images, a value between 400 and 800 works well.

Note that SurfFeatureDetector does not provide a way to select just a fixed number of keypoints. To do so, you have to set a low minHessian, sort the resulting keypoints by its Hessian value and then remove the least persistent ones.

翻译：为了判断一个点是否是surf特征点，需要在这个点周围构造一个hessian矩阵。决定这个矩阵的因素是hessian，它影响着特征点的鲁棒性。

minHessian是一个阈值，它决定了那些值是你接受的关键点。使用的时候，minHessian值越高，得到的关键点越少，但是关键点也就跟好。如果minHessian越小，得到的关键点会更多，但是关键点的质量也就不高。

一般，minHessian的值在400 和 800 之间。

注意：SurfFeatureDetector 并不会提供一个让你选择指定数目特征点的机会，你必须设定minHessian值，把关键点按Hessian值排序，然后去掉最不稳定的点。