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Let's take a look on this basic tutorial named Features2D + Homography to find a known object. It uses SurfFeatureDetector to detect features:

SurfFeatureDetector detector( minHessian ); std::vector<keypoint> keypoints_object, keypoints_scene; detector.detect( img_object, keypoints_object ); detector.detect( img_scene, keypoints_scene ); Then it uses SurfDescriptorExtractor to calculate descriptors (feature vectors) using detected features.

My questions are:

if I want to create my own feature detector (for example with Trajkovic or Harris algorithms) which Descriptor Extractor shall I use? are the features, that were found in SurfFeatureDetector, just the common points or the areas of points?

Let's take a look on this basic tutorial named Features2D + Homography to find a known object. It uses SurfFeatureDetector to detect features:

 SurfFeatureDetector detector( minHessian );
std::vector<keypoint> std::vector<KeyPoint> keypoints_object, keypoints_scene;
detector.detect( img_object, keypoints_object );
detector.detect( img_scene, keypoints_scene );