I am doing an experiment in which I have multiple cameras targeted to capture scenes as they are capturing them from a 2D plane. But the problem is their orientation which may change because of some movement and my target is to rectify those orientations only not translation.
I am trying to approach this problem in two ways:
I am trying to estimate relative camera pose from MATLAB function and then transforming the second image using T i.e. KRK^-1.
Second I am trying to address this problem using regular camera rectification problem using estimateUncalibratedRectification. The function return two transformations, T1 and T2, which can be directly used to warp two images. Instead of applying transformation on image separately what I am doing is to only apply transformation on second image and the transformation matrix is T1^-1*T2. My assumptions is that in this way I will have camera orientation equals to 0.
I am verifying if the transformations are correct by observing the farthest feature point which must be near to each other.
What problems I am facing using the first approach is:
Not getting the farthest point to 0.
Please correct my approach.
Related
I am using an API to analyze faces in Matlab, where I get for each picture a 3X3 rotation matrix of the face's orientation, telling which direction the head is pointing.
I am trying to normalize the image according to that matrix, so that it will be distorted to get the image of the face's plane. This is something like 'undoing' the projection of the face to the camera plane. For example, if the head is directed a little to the left, it will stretch the left side to (more or less) preserve the face's original proportions.
Tried using 'affine2d' and 'projective2d' with 'imwarp', but it didn't achieve that goal
Achieving your goal with simple tools like affine transformations seems impossible to me since a face is hardly a flat surface. An extreme example: Imagine the camera recording a profile view of someone's head. How are you going to reconstruct the missing half of the face?
There have been successful attempts to change the orientation of faces in images and real-time video, but the methods used are quite complex:
[We] propose a gaze correction method that needs just a
single webcam. We apply recent shape deformation techniques
to generate a 3D face model that matches the user’s face. We
then render a gaze-corrected version of this face model and
seamlessly insert it into the original image.
(Giger et al., https://graphics.ethz.ch/Downloads/Publications/Papers/2014/Gig14a/Gig14a.pdf)
Which Matlab functions or examples should be used to (1) track distance from moving object to stereo (binocular) cameras, and (2) track centroid (X,Y,Z) of moving objects, ideally in the range of 0.6m to 6m. from cameras?
I've used the Matlab example that uses the PeopleDetector function, but this becomes inaccurate when a person is within 2m. because it begins clipping heads and legs.
The first thing that you need deal with, is in how detect the object of interest (I suppose you have resolved this issue). There are a lot of approaches of how to detect moving objects. If your cameras will stand in a fix position you can work only with one camera and use some background subtraction to get the objects that appear in the scene (Some info here). If your cameras are are moving, I think the best approach is to work with optical flow of the two cameras (instead to use a previous frame to get the flow map, the stereo pair images are used to get the optical flow map in each fame).
In MatLab, there is an option called disparity computation, this could help you to try to detect the objects in scene, after this you need to add a stage to extract the objects of your interest, you can use some thresholds. Once you have the desired objects, you need to put them in a binary mask. In this mask you can use some image momentum (Check this and this) extractor to calculate the centroids. If the images in the binary mask look noissy you can use some morphological operations to improve the reults (watch this).
Recently, I have to do a project of multi view 3D scanning within this 2 weeks and I searched through all the books, journals and websites for 3D reconstruction including Mathworks examples and so on. I written a coding to track matched points between two images and reconstruct them into 3D plot. However, despite of using detectSURFFeatures() and extractFeatures() functions, still some of the object points are not tracked. How can I reconstruct them also in my 3D model?
What you are looking for is called "dense reconstruction". The best way to do this is with calibrated cameras. Then you can rectify the images, compute disparity for every pixel (in theory), and then get 3D world coordinates for every pixel. Please check out this Stereo Calibration and Scene Reconstruction example.
The tracking approach you are using is fine but will only get sparse correspondences. The idea is that you would use the best of these to try to determine the difference in camera orientation between the two images. You can then use the camera orientation to get better matches and ultimately to produce a dense match which you can use to produce a depth image.
Tracking every point in an image from frame to frame is hard (its called scene flow) and you won't achieve it by identifying individual features (such as SURF, ORB, Freak, SIFT etc.) because these features are by definition 'special' in that they can be clearly identified between images.
If you have access to the Computer Vision Toolbox of Matlab you could use their matching functions.
You can start for example by checking out this article about disparity and the related matlab functions.
In addition you can read about different matching techniques such as block matching, semi-global block matching and global optimization procedures. Just to name a few keywords. But be aware that the topic of stereo matching is huge one.
I am developing an app which uses LK for tracking and POSIT for estimation. I am successful in getting rotation matrix, projection matrix and able to track perfectly but the problem for me is I am not able to translate 3D object properly. The object is not fitting in to the right place where it has to fit.
Will some one help me regarding this?
Check this links, they may provide you some ideas.
http://computer-vision-talks.com/2011/11/pose-estimation-problem/
http://www.morethantechnical.com/2010/11/10/20-lines-ar-in-opencv-wcode/
Now, you must also check whether the intrinsic camera parameters are correct. Even a small error in estimating the field of view can cause troubles when trying to reconstruct 3D space. And from your details, it seems that the problem are bad fov angles (field of view).
You can try to measure them, or feed the half or double value to your algorithm.
There are two conventions for fov: half-angle (from image center to top or left, or from bottom to top, respectively from left to right) Maybe you just mixed them up, using full-angle instead of half, or vice-versa
Maybe you can show us how you build a transformation matrix from R and T components?
Remember, that cv::solvePnP function returns inverse transformation (e.g camera in world) - it finds object pose in 3D space where camera is in (0;0;0). For almost all cases you need inverse it to get correct result: {Rt; -T}
I have a computer vision set up with two cameras. One of this cameras is a time of flight camera. It gives me the depth of the scene at every pixel. The other camera is standard camera giving me a colour image of the scene.
We would like to use the depth information to remove some areas from the colour image. We plan on object, person and hand tracking in the colour image and want to remove far away background pixel with the help of the time of flight camera. It is not sure yet if the cameras can be aligned in a parallel set up.
We could use OpenCv or Matlab for the calculations.
I read a lot about rectification, Epipolargeometry etc but I still have problems to see the steps I have to take to calculate the correspondence for every pixel.
What approach would you use, which functions can be used. In which steps would you divide the problem? Is there a tutorial or sample code available somewhere?
Update We plan on doing an automatic calibration using known markers placed in the scene
If you want robust correspondences, you should consider SIFT. There are several implementations in MATLAB - I use the Vedaldi-Fulkerson VL Feat library.
If you really need fast performance (and I think you don't), you should think about using OpenCV's SURF detector.
If you have any other questions, do ask. This other answer of mine might be useful.
PS: By correspondences, I'm assuming you want to find the coordinates of a projection of the same 3D point on both your images - i.e. the coordinates (i,j) of a pixel u_A in Image A and u_B in Image B which is a projection of the same point in 3D.