I've been trying for a while to detect an image that looks like this:
Unfortunately i haven't been very lucky.
This image has to be detected so that I can "crop" a scene to whatever is below it.
I've been trying different feature detectors, like ORB, FAST and BRISK. And although I'm getting some key-points that look promising, when I try to find the Homography and apply the perspective transform to calculate my "scene corners" the results make absolutely no sense.
I suspect that the issue might be in how "simple" the marker is? since the corner points of the image are like technically the same, and within the small triangle in the middle, they are also very alike.
I'm looking for an advice or suggestion in how to approach this problem.
Edit:
My object to detect is an image. But the scene to be detected in is the video feed from an iOS camera device.
Edit 2:
I've replaced the top image with something more complex hoping the increase in keypoints would finally allow me to detect the object, but still no luck.
Here is the new top image:
And this is a sample of a frame: (taken as a screenshot of the iphone screen)
Keypoints detected:
Related
I'm fairly new to Computer Vision and OpenCV. I'm working on a project in which we are building a robot that plays snooker. We installed a camera on top of the table, with the aim of detecting the balls. I originally assumed that getting rid of the barrel distortion would be quite straight forward. However, I'm not very satisfied with the results I'm getting. In the second image I attached, one can clearly see, that after applying the undistortion transformation, the sides of the table are not parallel to each other. Moreover, with respect to the first image, the balls are deformed into a sort of egg shape.
Does anyone have an idea of how I could fix this issues? I tried to take more picture of the chessboard patter is as many different positions as possible, without any visible changes. Also using more parameters to model the distortion didn't seem to yield any improvements.
Distorted Image
Undistorted Image
I've 2 pictures of the same scene from an uncalibrated camera. The pics are from a slightly different angle and scale(zoom) and I'd like to superpose them, rejecting any kind of shake. In other words, I should transform them so the shake becomes imperceptible, do a Motion Compensation.
I've already tried using a simple SURF (feature) detector along with Homography but sometimes the result isn't satisfactory. So I am thinking about trying Image Rectification to compensate the motion.
- Would it work with slight changes, such as user shake?
- Would it really work to reject shake for these 2 frames? And for a bigger buffer of pictures (10 maybe)?
- Anyone knows if it would fix scale disparity (different zoom in the images)?
- What the algorithm really do? Will it transform both pictures into a third orientation?
If there is a better solution, I would be glad to know =)
EDIT
I don't aim to compensate blur motion but the displacement itself. For example, in this file the author compensates the angle difference between two cameras by Image Rectification. How does it actually work? Does it always create an intermediate picture orientation or can I specify that one of the pictures shall remains still??
Also, would I be able to apply this to many frames or it would always find an intermediate orientation for each two frames I put in?
Cheers,
I'm not sure how well superimposing the images would work. Another way to remove blur (including motion blur which should dominate in handheld camera devices) from an image is by blind deconvolution. It is basically a method of finding the inverse of the blur filter that was physically applied (camera shaken) to the real image. There's plenty of techniques out on the web. I've specifically had good results using a modified version of the algorithm in this paper: http://www.cse.cuhk.edu.hk/~leojia/all_final_papers/motion_deblur_cvpr07.pdf
It also comes with an executable file somewhere around the web so you can see if it's fit for your purpose.
Good luck out there!
I am working on images that are partially blur on some sections. These are noises that should be taken care of, but here is the problem:
Are there methods to detect whether an image is blur or partially blur at some sections of an image? For instance, take a look at sample image below:
You can see in the image that there are 3 sections that are visually blur: bottom-left, near center region and top-right. Now, is it possible to detect that any portion of an image is blur programming-wise or mathematically?
As lain_b pointed out, with an image like this you can use an edge detector and look for an absence of edges. I tried it on your image and it seems to work pretty well. First I used the kernel
[0,1,0,
1,-4,1,
0,1,0]
Which is a simple edge detector. Its result was
Then I used a threshold to get
Then I closed the image and opened it to get
This is obviously not a finished version, the top right portion did not recognize well at all. Perhaps you could improve it by blurring before performing thresholding, or by choosing better values for the threshold and the radii of the opening and closing operations. A lot of the decisions you will need to make depend on the constraints you can put on your problem. I think this technique will work for you though.
Edit
If you are looking for blur detection of arbitrary images you are going to have to investigate a wide variety of techniques. Things are much easier if you can make assumptions about your set of input images. Without any assumptions I don't know what will work best for you. Here is some reading on the topic
Image Blur Metrics
Reserach paper on using the Harr wavelet transform
Similar SO Question and look at the question that question links to
Blur detection is a very active research field, there is no one answer. You will just need to try all the methods you can find (these were found by googling detect blur in image).
This paper may be of some help. It does blur estimation (mostly for out of focus, but I think it also does blur) to recreate a similarly blurred object in the image.
I think you should be able to use it to detect the blurred areas, and how blurred they are. It should be especially relevent to your problem as it is designed to work with real-world images.
following up on my other question, do you guys know a good example in OpenCV, with a simple Black/White-Calibration Picture and appropriate detection-algorithms?
I just want to show some B&W-image on a screen, take a picture of that image from afar and calculate the size of the shown image, to calculate the distance to said screen.
Before I invent the wheel again, I recon this is so easy that it could be achieved through many different ways in OpenCV, yet I thought I'd ask if there's a preferred way around, possibly with some sample code.
(I got some face-detection code running using haarcascade-xml files already)
PS: I already have the resolution/dpi-part of my screen covered, so I know how big a picture would be in cm on my screen.
EDIT:
I'll make it real simple, I need:
A pattern, that is easily recognizable in an Image. Right now I'm experimenting with a checkerboard. The people who made ARDefender used this.
An appropriate algorithm to tell me the exact pixel coordinates of pattern 1) in a picture using OpenCV.
Well, it's hard to say which image is the best for recognition - in different illumination any color could be interpret as another color. Simple example:
As you can see both traffic signs have red color border but even on one image upper sign border is obviously not red.
So in my opinion you should use image with many different colors (like a rainbow). And also you said that it should be easy recognizable in different angles. That's why circle shape is the best for it.
That's why your image should look like this:
So idea of detection such object is the following:
Make different color segmentation (blue, red, green etc). For this use HSV color space.
Detect circles of specific color on image.
That area which has the biggest count of circles seems to be your object.
you just have to take pictures of your B&W object from several known distances (1m, 2m, 3m, ...) and then for each distance check the size of your object in the corresponding image.
From those datas, you will be able to create a linear function giving you the distance from the size in pixels (y = ax + b should do ;) ), translate it into your code and you're done.
Cheers
I am trying to track motion of a toy car. I have recorded few videos and now trying to calculate rotation.
My problem is extracting features from object surface is quit challenging due to motion blur. Below image shows a cropped image from a video frame. The distortion happen in horizontal lines. The distortion seen in this image happens when object is moving. When the object is not moving there is no distortion.
Image shows distorted image of the car when its moving forward in a diagonal path cross the image frame.
I tried a wiener filter, based on median and variance but it didn't do much improvement. It only gave me a smoothed image as if Gaussian blur was applied on it.
What type of enhancements should I do to get a better image?
video - 720 x 576 frames - 25fps
from the picture provided it looks like you need to de-interlace the video rather than just trying to filter what's there; i remember doing this by just taking every other scan line and then doing a resize to put it back in perspective.
i found a pretty cool site that talks about deinterlacing in case you'd like to see if you might have other possibilities:
http://www.100fps.com/
(oh, and i have not inspected the image very closely so it's possible that there is some other interlacing scheme going on than just every other line; in which case my first answer wouldn't work properly. and it does imply that you will lose some resolution but that's just the nature of interlaced video...)
Given that your camera outputs interlaced video, you are better off using one field of the video. Either only use the even lines of the image or only the odd lines. The image will be squashed but you won't be mixing two images together.
Yep, that image needs to be de-interlaced. Correcting "distortion" due to linear movement is a different thing, you need to do a linear directional filtering depending on the speed of the vehicle, the distance to the camera and the obturation speed.
You have to first calculate the impulse response for a given set of conditions (those above, which represent the deviation or the distance between the same point taken at the beggining of the capture and the end of it), and then apply the inverse filtering. You may need to use some filtering or image processing toolkit, if using Matlab it's going to be easy.
Did you try:
deconvblind
Follow the example on deconvblind mathworks. It might work well on your example image.
Another example - Image Restoration
The following algorithm is a very simple de-interlaceing method:
cv::Mat input = cv::imread("img.jpg");
cv::Mat tmp(input.rows/2, input.cols*2, input.type(), input.data);
tmp = tmp.colRange(0, input.cols);
cv::Mat output;
cv::resize(tmp, output, Size(), 1, 2);