I am using open cv and C++. I have 2 face images which contain marker points on them. I have already found the coordinates of the marker points. Now I need to align those 2 face images based on those coordinates. The 2 images may not be necessarily of the same height, that is why I can't figure out how to start aligning them, what should be done etc.
In your case, you cannot apply the homography based alignment procedure. Why not? Because it does not fit in this use case. It was designed to align flat surfaces. Faces (3D objects) with markers at different places and depths are clearly no planar surface.
Instead, you can:
try to match the markers between images then interpolate the displacement field of the other pixels. Classical ways of doing it will include moving least squares interpolation or RBF's;
otherwise, a more "Face Processing" way of doing it would be to use the decomposition of faces images between a texture and a face model (like AAM does) and work using the decomposition of your faces in this setup.
Define "align".
Or rather, notice that there does not exist a unique warp of the face-side image that matches the overlapping parts of the frontal one - meaning that there are infinite such warps.
So you need to better specify what your goal is, and what extra information you have, in addition to the images and a few matched points on them. For example, is your camera setup calibrated? I.e do you know the focal lengths of the cameras and their relative position and poses?
Are you trying to build a texture map (e.g. a projective one) so you can plaster a "merged" face image on top of a 3d model that you already have? Then you may want to look into cylindrical or spherical maps, and build a cylindrical or spherical projection of your images from their calibrated poses.
Or are you trying to reconstruct the whole 3d shape of the head based on those 2 views? Obviously you can do this only over the small strip where the two images overlap, and they quality of the images you posted seems a little too poor for that.
Or...?
Related
I have written an algorithm to process a camera capture and extract a binary image of two features I'm interested in. I'm trying to find the best (fastest) way of detecting when the two features intersect and where the lowest (y coordinate is greatest) point is (this will be the intersection).
I do not want to use a findContours() based method as this is too slow and, in my opinion, unnecessary. I also think blob detection libraries are too bloated for this.
I have two sample images (sorry for low quality):
(not touching: http://i.imgur.com/7bQ9qMo.jpg)
(touching: http://i.imgur.com/tuSmKw7.jpg)
Due to the way these images are created, there is often noise in the top right corner which looks like pixelated lines but methods such as dilation and erosion lose resolution around the features I'm trying to find.
My initial thought would be to use direct pixel access to form a width filter and a height filter. The lowest point in the image is therefore the intersection.
I have no idea how to detect when they touch... logically I can see that a triangle is formed when they intersect and otherwise there is no enclosed black area. Can I fill the image starting from the corner with say, red, and then calculate how much of the image is still black?
Does anyone have any suggestions?
Thanks
Your suggestion is a way more slow than finding contours. For binary images, finding contour is very easy and quick because you just need to find a black pixel followed by a white pixel or vice versa.
Anyway, if you don't want to use it, you can use the vertical projection or vertical profile you will see it the objects intersect or not.
For example, in the following image check the the letter "n" which is little similar to non-intersecting object, and the letter "o" which is similar to intersecting objects :
By analyzing the histograms you can recognize which one is intersecting or not.
Hi i had computed the fundamental matrix from two images and i found out that the epipoles lie within the image. I cannot do the rectification using matlab if the image contains epipole.
May i know how to compute the fundamental matrix that the epipole is not in the image?
The epipolar geometry is the intrinsic projective geometry between two
views. It is independent of scene structure, and only depends on the
cameras' internal parameters and relative pose.
So the intrinsics/extrinsics of the cameras define the fundamental matrix that you get (i.e. you cannot compute another fundamental, s.t. the epipoles are not in the image).
What you can do is either take a different pair of images (with a different camera geometry, for example) and you may get epipoles out of the image.
The problem you're actually having is that the rectification algorithm that you're using is limited and doesn't work for the case when the epipole is inside the image. Note, there exist other algorithms that do not have this limitation. I have implemented such an algorithm in the past, and may be can find the (MATLAB) code. So, please let me know if you're interested.
If you're in a mood to learn more about epipolar geometry and the fundamental matrix, I recommend you take a look here:
I am currently reading into the topic of stereo vision, using the book of Hartley&Zimmerman alongside some papers, as I am trying to develop an algorithm capable of creating elevation maps from two images.
I am trying to come up with the basic steps for such an algorithm. This is what I think I have to do:
If I have two images I somehow have to find the fundamental matrix, F, in order to find the actual elevation values at all points from triangulation later on. If the cameras are calibrated this is straightforward if not it is slightly more complex (plenty of methods for this can be found in H&Z).
It is necessary to know F in order to obtain the epipolar lines. These are lines that are used in order to find image point x in the first image back in the second image.
Now comes the part were it gets a bit confusing for me:
Now I would start taking a image point x_i in the first picture and try to find the corresponding point x_i’ in the second picture, using some matching algorithm. Using triangulation it is now possible to compute the real world point X and from that it’s elevation. This process will be repeated for every pixel in the right image.
In the perfect world (no noise etc) triangulation will be done based on
x1=P1X
x2=P2X
In the real world it is necessary to find a best fit instead.
Doing this for all pixels will lead to the complete elevation map as desired, some pixels will however be impossible to match and therefore can't be triangulated.
What confuses me most is that I have the feeling that Hartley&Zimmerman skip the entire discussion on how to obtain your point correspondences (matching?) and that the papers I read in addition to the book talk a lot about disparity maps which aren’t mentioned in H&Z at all. However I think I understood correctly that the disparity is simply the difference x1_i- x2_i?
Is this approach correct, and if not where did I make mistakes?
Your approach is in general correct.
You can think of a stereo camera system as two points in space where their relative orientation is known. This are the optical centers. In front of each optical center, you have a coordinate system. These are the image planes. When you have found two corresponding pixels, you can then calculate a line for each pixel, wich goes throug the pixel and the respectively optical center. Where the two lines intersect, there is the object point in 3D. Because of the not perfect world, they will probably not intersect and one may use the point where the lines are closest to each other.
There exist several algorithms to detect which points correspond.
When using disparities, the two image planes need to be aligned such that the images are parallel and each row in image 1 corresponds to the same row in image 2. Then correspondences only need to be searched on a per row basis. Then it is also enough to know about the differences on x-axis of the single corresponding points. This is then the disparity.
Finding Circle Edges :
Here are the two sample images that i have posted.
Need to find the edges of the circle:
Does it possible to develop one generic circle algorithm,that could find all possible circles in all scenarios ?? Like below
1. Circle may in different color ( White , Black , Gray , Red)
2. Background color may be different
3. Different in its size
http://postimage.org/image/tddhvs8c5/
http://postimage.org/image/8kdxqiiyb/
Please suggest some idea to write a algorithm that should work out on above circle
Sounds like a job for the Hough circle transform:
I have not used it myself so far, but it is included in OpenCV. Among other parameters, you can give it a minimum and maximum radius.
Here are links to documentation and a tutorial.
I'd imagine your second example picture will be very hard to detect though
You could apply an edge detection transformation to both images.
Here is what I did in Paint.NET using the outline effect:
You could test edge detect too but that requires more contrast in the images.
Another thing to take into consideration is what it exactly is that you want to detect; in the first image, do you want to detect the white ring or the disc inside. In the second image; do you want to detect the all the circles (there are many tiny ones) or just the big one(s). These requirement will influence what transformation to use and how to initialize these.
After transforming the images into versions that 'highlight' the circles you'll need an algorithm to find them.
Again, there are more options than just one. Here is a paper describing an algoritm
Searching the web for image processing circle recognition gives lots of results.
I think you will have to use a couple of different feature calculations that can be used for segmentation. I the first picture the circle is recognizeable by intensity alone so that one is easy. In the second picture it is mostly the texture that differentiates the circle edge, in that case a feature image based based on some kind of texture filter will be needed, calculating the local variance for instance will result in a scalar image that can segment out the circle. If there are other features that defines the circle in other scenarios (different colors for background foreground etc) you might need other explicit filters that give a scalar difference for those cases.
When you have scalar images where the circles stand out you can use the circular Hough transform to find the circle. Either run it for different circle sizes or modify it to detect a range of sizes.
If you know that there will be only one circle and you know the kind of noise that will be present (vertical/horizontal lines etc) an alternative approach is to design a more specific algorithm e.g. filter out the noise and find center of gravity etc.
Answer to comment:
The idea is to separate the algorithm into independent stages. I do not know how the specific algorithm you have works but presumably it could take a binary or grayscale image where high values means pixel part of circle and low values pixel not part of circle, the present algorithm also needs to give some kind of confidence value on the circle it finds. This present algorithm would then represent some stage(s) at the end of the complete algorithm. You will then have to add the first stage which is to generate feature images for all kind of input you want to handle. For the two examples it should suffice with one intensity image (simply grayscale) and one image where each pixel represents the local variance. In the color case do a color transform an use the hue value perhaps? For every input feed all feature images to the later stage, use the confidence value to select the most likely candidate. If you have other unknowns that your algorithm need as input parameters (circle size etc) just iterate over the possible values and make sure your later stages returns confidence values.
I am currently researching the use of a low resolution camera facing vertically at the ground (fixed height) to measure the speed (speed of the camera passing over the surface). Using OpenCV 2.1 with C++.
Since the entire background will be constantly moving, translating and/or rotating between consequtive frames, what would be the most suitable method in determining the displacement of the frames in a 'useable value' form? (Function that returns frame displacement?) Then based on the height of the camera and the frame area captured (dimensions of the frame in real world), I would be able to calculate the displacement in the real world based on the frame displacement, then calculating the speed for a measured time interval.
Trying to determine my method of approach or if any example code is available, converting a frame displacement (or displacement of a set of pixels) into a distance displacement based on the height of the camera.
Thanks,
Josh.
It depends on your knowledge in computer vision. For the start, I would use what opencv can offer. please have a look at the feature2d module.
What you need is to first extract feature points (e.g. sift or surf), then use its build in matching algorithms to match points extracted from two frames. Each match will give you some constraints, and you will end up solving a over-saturated Ax=B.
Of course, do your experiments offline, i.e. shooting a video first and then operate on the single images.
UPDATE:
In case of mulit-camera calibration, your goal is to determine the 3D location of each camera, which is exactly what you have. Imagine instead of moving your single camera around, you have as many cameras as the number of images in the video captured by your single camera and you want to know the 3D location of each camera location, which represent the location of each image being taken by your single moving camera.
There is a matrix where you can map any 3D point in the world to a 2D point on your image see wiki. The camera matrix consists of 2 parts, intrinsic and extrinsic parameters. I (maybe inexactly) referred intrinsic parameter as the internal matrix. The intrinsic parameters consists of static parameters for a single camera (e.g. focal length), while the extrinsic ones consists of the location and rotation of your camera.
Now, once you have the intrinsic parameters of your camera and the matched points, you can then stack a lot of those projection equations on top of each other and solve the system for both the actual 3D location of all your matched points and all the extrinsic parameters.
Given interest points as described above, you can find the translational transformation with opevcv's findHomography.
Also, if you can assume that transformations will be somewhat small and near-linear, you can just compare image pixels of two consecutive frames to find the best match. With enough downsampling, this doesn't take too long, and from my experience works rather well.
Good luck!