homography

I've done image warping using OpenCV in Python and C++, see the Coca Cola logo warped in place in the corners I had selected: Using the following images: and this: Full album with transition pics and description here I need to do exactly this, but in OpenGL. I'll have: Corners inside which I've to map the warped image A homography matrix that maps the transformation of the logo image into the logo image you see inside the final image (using OpenCV's warpPerspective), something like this: [[ 2.59952324e+00, 3.33170976e-01, -2.17014066e+02], [ 8.64133587e-01, 1.82580111e+00, -3.20053715e+02], [

I am able to track 4 coordinates over different images of the same scene by calculating a 3x3 homography between them. Doing this I can overlay other 2D images onto these coordinates. I am wondering if I could use this homography to augment a cube onto this position instead using opengl? I think the 3x3 matrix doesn't give enough information but if I know the camera calibration matrix can I get enough to create a model view matrix to do this? Thank you for any help you can give. If you have the camera calibration matrix (intrinsic parameters) and the homography, since the homography (between

I am working with my Kinect on some 2D 3D image processing. Here is my problem: I have points in 3D (x,y,z) which lie on a plane. I also know the coordinates of the points on the RGB image (x,y). Now I want to estimate a 2D-3D homography matrix to estimate the (x1,y1,z1) coordinates to a random (x1,y1) point. I think that is possible, but I don't know where to start. Thanks! What you're looking for is a camera projection matrix , not a homography . A homography maps a plane seen from a camera to the same plane seen from another. For estimating the camera matrix, look up solutions to solving

I'm trying to compute Homography matrix H given a set of correspondences and the Fundamental matrix F . From the principle of Epipolar geometry I know this can be done by cross product of epiline and F from Epipole Geometry [e_ij] x F_ij = H_ij I'm using OpenCV for finding Fundamental matrix F from set of matches between two views using cv::findFundamentalMat() . My question is that how can I find e_ij and how to use it in order to compute H . In OpenCV there is a function cv::computeCorrespondEpilines() that finds epilines corespond to each given point. It's worth mentioning that I'm not

Libs: OpenCV Target: Android (OpenCV4Android) I try to compute the Homography of a world plane (e.g. monitor screen) to get the camera pose, transform it and reproject the points back for tracking tasks. I'm using OpenCVs findHomography() / getPerspectiveTransform() to get the homography. The reprojection of the points using perspectiveTransform() (as explained here: http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html ) which works pretty well. The "screenPoints" are the world coordinates of the monitor edges (using the aspect ratio and a z-value of 0)

Over the past weeks I've tried learning to rectify images and with the help of the people here I've managed to understand it better. About a week ago I set up a test example which I wanted to rectify (view the image from above). This worked fine (original: http://sitedezign.net/original.jpg and rectified: http://sitedezign.net/rectified.jpg ) with the function T = cv2.getPerspectiveTransform(UV_cp, XYZ_gcp) where T becomes the Homography. When I tried to do this with real world photos it failed because the realworld coordinates aren't perfectly on a plane (but simply around 10 control points