I have two images from the same video sequence.
I find correspondent key-points on them as follows
My goal is to detect the homography that relates both ground planes in image 1 and image 2 (without calibrating the camera).
So my current problem is: How can I know which points are on the ground plane? or How can I detect correspondant points that lies on the ground plane?
THANK YOU IN ADVANCE!
Related
I have rotating camera images and I'm trying this example of a MATLAB computer vision toolbox (https://www.mathworks.com/matlabcentral/fileexchange/67383-stereo-triangulation)
I have the calibration and rotation matrix for each image, however I always find 3d points equal to (0,0,0).
It is noted that the translation is null which makes the fourth column null.
You cannot reconstruct a 3D point from a rotating camera.
I suggest you try and draw an example. The idea of triangulation is to compute the intersection of two backprojection rays. These rays pass through the camera center and the point to be reconstructed. In your drawing, you'll find that the intersection becomes more and more accurate the larger the so-called stereo baseline is (that's the translation from one camera center to the other).
Now, for a rotating camera, the camera center remains the same and therefore, the two rays are identical. An intersection is not defined.
I am supposed to determine the direction of a boat from drone imagery, whether it's docked from the front or from the back
I tried to split the bbox of the boat image, use binary images by thresholding the boat bbox,
and i tried to split the bbox into two half, calculate the sum of blue pixels in every half sinc the front part of the boat will have more water i the image due to the triangle shape, but it didn't work
My question is, how can I determine the correct direction of the boat using image processing techniques
I used this TensorFlow library to detect object orientation in many projects. You need to train a neural network on your images. Then it will predict both boat location and direction.
use semantic segmentation to detect the dock
and a keypoint method to detect the boats, this method is usually used for face recognition but I think it would help in your case
A bit of background
I am writing a simple ray tracer in C++. I have most of the core complete but don't understand how to retrieve the world coordinate of a pixel on the image plane. I need this location so that I can cast the ray into the world.
Currently I have a Camera with a position(aka my perspective reference point), a direction (vector) which is not normalized. The directions length signifies the center of the image plane and which way the camera is facing.
There are other values associated with the camera but they should not be relevant.
My image coordinates will range from -1 to 1 and the perspective(focal length), will change based on the distance of the direction associated with the camera.
What I need help with
I need to go from pixel coordinates (say [0, 256] in an image 256 pixels on each side) to my world coordinates.
I will also want to program this so that no matter where the camera is placed and where it is directed, that I can find the pixel in the world coordinates. (Currently the camera will almost always be centered at the origin and will look down the negative z axis. I would like to program this with the future changes in mind.) It is also important to know if this code should be pushed down into my threaded code as well. Otherwise it will be calculated by the main thread and then the ray will be used in the threaded code.
(source: in.tum.de)
I did not make this image and it is only there to give an idea of what I need.
Please leave comments if you need any additional info. Otherwise I would like a simple theory/code example of what to do.
Basically you have to do the inverse process of V * MVP which transforms the point to unit cube dimensions. Look at the following urls for programming help
http://nehe.gamedev.net/article/using_gluunproject/16013/ https://sites.google.com/site/vamsikrishnav/gluunproject
I am currently working on a program to detect coordinates of pool balls in an image of a pool table taken from an arbitrary point.
I first calculated the table corners and warped the perspective of the image to obtain a bird's eye view. Unfortunately, this made the spherical balls appear to be slightly elliptical as shown below.
In an attempt to detect the ellipses, I extracted all but the green felt area and used a Hough transform algorithm (HoughCircles) on the resulting image shown below. Unfortunately, none of the ellipses were detected (I can only assume because they are not circles).
Is there any better method of detecting the balls in this image? I am technically using JavaCV, but OpenCV solutions should be suitable. Thank you so much for reading.
The extracted BW image is good but it needs some morphological filters to eliminate noises then you can extract external contours of each object (by cvFindContours) and fit best ellipse to them (by cvFitEllipse2).
The issue we are trying to solve the issue of locating a point in two different representations of a plane. The first plane we have is rotated to create perspective; the second is a 2d view of that same plane. We have 4 points on each of the plans that we know to be equivalent. The question is if we have an arbitrary point in plane 1, how do we find the corresponding point in plane 2?
It is best probably to illustrate the use case in order to best clarify the question. We have an image illustrated on the left.
Projective plane
2D layout diagram of space
So the givens that we have are the red squares from both pictures. Note that if possible, I’d like it to be possible that the 2D space isn’t necessarily a square. These are available to us ahead of time and known. I also have green dots laid out on the plane in the first image. I’d like to be able to do a projection of the dot in image 1 onto the space in image 2.
Note also for the image 1 I do not have a defined window or eye position. I just know that the red square from image 1 is a transform of the red square form image 2 and that the image 2 is in 2D space.
This is a special case of finding mappings between quadrilaterals that preserve straight lines. These are generally called homographic or projective transforms. Here, one of the quads is a square, so this is a popular special case. You can google these terms ("quad to quad", etc) to find explanations and code, but here are some for you.
Perspective Transform Estimation
a gaming forum discussion
extracting a quadrilateral image to a rectangle
Projective Mappings for Image Warping by Paul Heckbert.
The math isn't particularly pleasant, but it isn't that hard either. You can also find some code from one of the above links.
Update
And this is one of my favorites: Computing a projective transformation