image-stabilization

I have been working on video stabilization for quite a few weeks now. The algorithm I'm following basically involves 3 steps :- 1. FAST feature detection and Matching 2. Calculating affine transformation (scale + rotation + translation x + translation y ) from matched keypoints 3. Smooth motion parameters using cubic spline or b-spline. I have been able to calculate affine transform. But I am stuck at smoothing motion parameters. I have been unable to evaluate spline function to smooth the three parameters. Here is a graph for smoothed data points Any suggestion or help as to how can I code to

I'm trying to perform a complex warp of an image using Dense Optical Flow (I am trying to wap the second image into roughly the same shape as the first image). I'm probably getting this all wrong but Ill post up what I've tried: cv::Mat flow; cv::calcOpticalFlowFarneback( mGrayFrame1, mGrayFrame2, flow, 0.5, 3, 15, 3, 5, 1.2, 0 ); cv::Mat newFrame = cv::Mat::zeros( frame.rows, frame.cols, frame.type() ); cv:remap( frame, newFrame, flow, cv::Mat(), CV_INTER_LINEAR ); The idea if that I am calculating the flow mat from 2 gray scale frames. I get back a flow mat which seems to make sense but now

From what I have read on the web, one of the most efficient algorithms for image stabilization is to use Gray coded bit plane matching. However, I'm having trouble understanding it (Gray codes themselves are not that complex, it's the rest of it). Can anyone point me to a resource on this subject (or another good method of stabalization) that is a little below the level of most published papers? Sample code beats abstract generalized equations. For my purpose, there will be no panning or zooming of the video and no moving objects in the frames. zerm You can try some simple approaches first, I

I'm using openCV's estimateRigidTransform to stabilize a series of video frames, which works very well. The function returns a 2x3 transformation matrix M: | a_11 a_12 b_1 | | -a_12 a_11 b_2 | As far as I understand the b elements represent the translation, and the a elements are used to do the scaling and rotation. What I would like to do, is get global scale from M - a value that represents the camera zooming factor. How can I do calculate this correctly from a values? To clarify a little more what I want to get, here is an example. Let's say estimateRigidTransform calculates a