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pattern recognition to detect object position ?

asked 2013-06-17 04:30:31 -0500

altella gravatar image

updated 2013-06-17 07:51:37 -0500

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:

image description

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

image description

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 );

return 0;


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It would help us to see your actual code for matching the keypoints.

Notas gravatar imageNotas ( 2013-06-17 04:46:01 -0500 )edit

2 answers

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answered 2013-06-17 14:02:46 -0500

Guanta gravatar image

Your approach is in principle correct. However note that you will always get some matches between your images. Therefore you need to filter them out, additionally to your min_dist-test you can

  • use ratio-test
  • use cross-check (for FLANN you need to code it yourself, BFMatcher has an option for that)
  • try different RANSAC parameters
  • warp the image according to your homography you found and see how good this warped image matches the other one (e.g. via cross-correlation)
  • apply other geometrical constraints or verification steps

Maybe this similar question will also help you

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answered 2013-06-17 12:43:01 -0500

Victor1234 gravatar image

If you have approximately flat object to recognize, see sample

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Asked: 2013-06-17 04:30:31 -0500

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Last updated: Jun 17 '13