pattern recognition to detect object position ?

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Hello all;

I am trying to program a pattern recognition system using the features2d module also with nonfree module. My main objective is to detect the position of an object in a scene, given 5 models of different positions available. This algorithm must work translation, rotation and scale independent. I am using Surf detector as a first try, adjusting its parameters, and I obtain correct matches when the postion of the model and the position in the scene coincide. This can be seen in the following image:

however, when I use the same algorithm with another position, I also obtain matches which obviously are incorrect:

I want to detect the position of the object in the scene, but if I obtain matches in all the cases, it is impossible to know which is the real position. Is this approach correct for what I am intending to do? Any other good idea?

Thank you all very much in advance,

Best regards, Alberto

PD: I attach the code:

    int main( int argc, char** argv )
{


    Mat img_object = imread( "Pos2Model_Gray.png", CV_LOAD_IMAGE_GRAYSCALE );
    Mat img_scene = imread( "Kinect_grayscale_36.png", CV_LOAD_IMAGE_GRAYSCALE );

    //-- Step 1: Detect the keypoints using SURF Detector
    int minHessian = 800;
    std::vector<KeyPoint> keypoints_object, keypoints_scene;
    SurfFeatureDetector detector(minHessian);
    detector.detect(img_object, keypoints_object);
    detector.detect(img_scene, keypoints_scene);

    //-- Step 2: Calculate descriptors (feature vectors)
    SurfDescriptorExtractor extractor;
    Mat descriptors_object, descriptors_scene;
    extractor.compute( img_object, keypoints_object, descriptors_object );
    extractor.compute( img_scene, keypoints_scene, descriptors_scene );

    //-- Step 3: Matching descriptor vectors using FLANN matcher
    FlannBasedMatcher matcher;
    std::vector< DMatch > matches;
    matcher.match( descriptors_object, descriptors_scene, matches );



     //-- Quick calculation of max and min distances between keypoints
        double max_dist = 0; double min_dist = 100;
        for( int i = 0; i < descriptors_object.rows; i++ )
        { 
            double dist = matches[i].distance;
            if( dist < min_dist ) min_dist = dist;
            if( dist > max_dist ) max_dist = dist;
        }
        printf("-- Max dist : %f \n", max_dist );
        printf("-- Min dist : %f \n", min_dist );

    //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
    std::vector< DMatch > good_matches;
    for( int i = 0; i < descriptors_object.rows; i++ )
        if( matches[i].distance < 1.5 *min_dist )
            good_matches.push_back( matches[i]); 

    Mat img_matches;
    drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
               vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

    //-- Localize the object
    std::vector<Point2f> obj;
    std::vector<Point2f> scene;

    for( int i = 0; i < good_matches.size(); i++ )
    {
        //-- Get the keypoints from the good matches
        obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
        scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
    }

    Mat H = findHomography( obj, scene, CV_RANSAC );

    //-- Get the corners from the image_1 ( the object to be "detected" )
    std::vector<Point2f> obj_corners(4);
    obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
    obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
    std::vector<Point2f> scene_corners(4);

    perspectiveTransform( obj_corners, scene_corners, H);
    //-- Draw lines between the corners (the mapped object in the scene - image_2 )
    //-- Show detected matches
    imshow( "Good Matches & Object detection", img_matches );

    waitKey(0);
    return 0;
}