homography

Given two consecutive 3D point clouds 1 and 2 (not the whole cloud, say 100 points selected from the cloud with OpenCV's GoodFeaturesToMatch), obtained from a Kinect depthmap, I want to compute camera's homography from 1 to 2. I understand that this a projective transform, and it has already been done by many people: here (slide 12) , here (slide 30) and here in what seems to be the classic paper . My problem is that whilst I'm a competent programmer, I haven't got the math or trig skills to turn one of those methods into code. As this is not an easy problem, I offer a large bounty for the

I have already done the comparison of 2 images of same scene which are taken by one camera with different view angles(say left and right) using SURF in emgucv (C#). And it gave me a 3x3 homography matrix for 2D transformation. But now I want to make those 2 images in 3D environment (using DirectX). To do that I need to calculate relative location and orientation of 2nd image(right) to the 1st image(left) in 3D form. How can I calculate Rotation and Translate matrices for 2nd image? I need also z value for 2nd image. I read something called 'Homograhy decomposition'. Is it the way? Is there

I am using the function findHomography of OpenCV with the RANSAC method in order to find the homography that relates two images linked with a set of keypoints. Main issue is that I haven’t been able to find anywhere yet what are the values of the mask matrix that the function outputs. Only information that I know is that 0 values are outliers, and non zero values are inliers. But what does it mean the inliers value? Anyone knows? Thanks in advance! Piece of code where I call findHomography : cv::Mat H12; cv::Mat mask; H12 = cv::findHomography(FvPointsIm1, FvPointsIm2, mask, CV_RANSAC, 5); ui-

I've been running a project of stitching images from multiple cameras, but I think I've got a bottleneck...I have some questions about this issue. I wanna try to mount them on a vehicle in the future and that means the relative positions and orientations of cameras are FIXED. Also, as I'm using multiple cameras and try to stitch images from them using HOMOGRAPHY, I'll put cameras as close as possible so that the errors(due to the fact that the foci of the cameras are not at the same position and it's impossible as cameras occupy certain space.) can be reduced. Here's a short experiment video

I'm working with a homography calculated by OpenCV. I currently use this homography to transform points using the function below. This function performs the task I require however I have no clue how it actually works. Can anyone explain, line by line exactly, the logic/theory behind the last 3 lines of code, I understand that this transforms the point x,y but I'm unclear as to why this works: Why are Z , px and py calculated in this way, what do the elements in h correspond to? Your comments are greatly appreciated :) double h[9]; homography = cvMat(3, 3, CV_64F, h); CvMat ps1 = cvMat(MAX

This question is on the OpenCV functions findHomography , getPerspectiveTransform & getAffineTransform What is the difference between findHomography and getPerspectiveTransform ?. My understanding from the documentation is that getPerspectiveTransform computes the transform using 4 correspondences (which is the minimum required to compute a homography/perspective transform) where as findHomography computes the transform even if you provide more than 4 correspondencies (presumably using something like a least squares method?). Is this correct? (In which case the only reason OpenCV still