I have several images of the pugmark with lots of irrevelant background region. I cannot do intensity based algorithms to seperate background from the foreground.
I have tried several methods. one of them is detecting object in Homogeneous Intensity image
but this is not working with rough texture images like
http://img803.imageshack.us/img803/4654/p1030076b.jpg
http://imageshack.us/a/img802/5982/cub1.jpg
http://imageshack.us/a/img42/6530/cub2.jpg
Their could be three possible methods :
1) if i can reduce the roughness factor of the image and obtain the more smoother texture i.e more flat surface.
2) if i could detect the pugmark like shape in these images by defining rough pugmark shape in the database and then removing the background to obtain image like http://i.imgur.com/W0MFYmQ.png
3) if i could detect the regions with depth and separating them from the background based on difference in their depths.
please tell if any of these methods would work and if yes then how to implement them.
I have a hunch that this problem could benefit from using polynomial texture maps.
See here: http://www.hpl.hp.com/research/ptm/
You might want to consider top-down information in the process. See, for example, this work.
Looks like you're close enough from the pugmark, so I think that you should be able to detect pugmarks using Viola Jones algorithm. Maybe a PCA-like algorithm such as Eigenface would work too, even if you're not trying to recognize a particular pugmark it still can be used to tell whether or not there is a pugmark in the image.
Have you tried edge detection on your image ? I guess it should be possible to finetune Canny edge detector thresholds in order to get rid of the noise (if it's not good enough, low pass filter your image first), then do shape recognition on what remains (you would then be in the field of geometric feature learning and structural matching) Viola Jones and possibly PCA-like algorithm would be my first try though.
Related
Here are some images taken from experiments which show a bubble caused by spheres moving in liquid.
Now I want to get the area of the bubble from every image using Matlab. The first thing come to my mind is edge detection. So I tried using the following code:
A = imread('D:\1.jpg');
BW1 = edge(A,'sobel');
figure, imshow(BW1)
to get the cavity edge of the picture which was then cropped manually, as the picture show, the result (below) doesn't satisfy requirements. Also, I still don't know how to get the area of the bubble.
So, can someone tell me what should I do?
I think you should use background subtraction and try a simple segmentation.
You could use regionprops to get the area of the bubble:
https://www.mathworks.com/help/images/ref/regionprops.html
I feel like it should work pretty well. If you have a hard time obtaining a clean segmentation you could probably improve the experimental setup to increase the contrast of the bubble with respect to the background by choosing a background as dark as possible and using some lateral illumination to leverage the diffusion of the light by the bubble.
Finally the segmentation should be performed in a region of interest (ROI) since you know the bubble is confined within the tank
As for the issue of getting an accurate cavity edges, the computer vision system toolbox has the vision.ForegroundDetector object, which implements a variant of Stauffer and Grimson's GMM background subtraction. The implementation is very fast, leveraging multiple cores. Check out this example of how to use background subtraction.
As for the issue of finding the area of the bubble, use the bwarea command. https://www.mathworks.com/help/images/ref/bwarea.html, it will sum up all the white pixels in the image.
I believe background subtraction is the most efficient method to calculate this bubble area. Note that you may need to use opening and closing techniques afterwards to filter other regions see (imopen imclose) at: https://uk.mathworks.com/help/images/ref/imopen.html , and afterwards, you can apply bwarea to calculate area. You could also use impixelinfo command to compare intensity level of bubbles and other areas, and therefore, threshold image to extract bubbles. It works only when you have same threshold level for all images. Further, it is possible to combine all these techniques which is completely depended on your images to achieve better results.
Other shape-based techniques also can be used to extract bubble region area.
I have seen many tutorials that people blend two images that are placed on top of each other very nicely in Photoshop. For example here are two images that are placed on top of each other:
Then in Photoshop after some work, the edges (around the smaller image) will be erased and two images are nicely mixed.
For example, this is a possible end result:
As it can be seen there is no edge and two images are very nicely blended, without blurring.
Can someone point me to any article or post that shows the math behind it? If there is a MATLAB code that can do it, that would be even better. Or at least if someone can tell me what is the correct term for this so I can do Google search on the topic.
Straight alpha blending alone is not sufficient, as it will perform a uniform mixing of the two images.
To achieve nice-looking results, you will need to define an alpha map, i.e. an image of the same size where you adjust the degree of transparency depending on the image that should dominate.
To obtain the mask, you can draw it by hand, for example as a filled outline, as a path or a polygon. Then you have to strongly blur this mask to get a smooth blend.
It looks very difficult (if not impossible) to automate this, as no software can guess what you want to enhance.
The term you are looking for is alpha blending.
https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending
The maths behind it boils down to some alpha weighted sums.
Matlab provides the function imfuse to achieve this:
https://de.mathworks.com/help/images/ref/imfuse.html
Edit: (as it still seems to be unclear)
Let's say you have 2 images A and B wich you want to blend.
You put one image over the other so for each coordinate you have 2 RGB touples.
Now you need to define the weight of both images. Will you only see the colour of image A or B or which ratio will you choose to mix them?
This is done by alpha values.
So all you need is a 2d function that defines the mixing ratio for each pixel.
Usually you have values between 0 and 1 where 0 shows one image, 1 shows the other image, 0.5 will mix them both equally and so on...
Just read the article I have linked. It gives you a clear mathematical definition. I can't provide more detail than that.
If you have problems understanding that I urge you to read a book on image processing fundamentals.
At image i need find "table" - simple rectangle.
Problem is with edge recognition, because potencial photos will be "dark".
I tried edge - sobel, canny, log, .... - recognition and after that Hough transformation and line finding. But this algorithms are not enough for this task.
Something what can help me:
- it is rectangle!, only in perspective view (something like fitting perspective rectangle?)
- that object MUST cover atleast for example 90% of photo (i know i need looking near photo edges)
- that rectangle have fast same color (for example wood dining table)
- i need find atleast "only" 4 corners..(but yes, better will be find the edges of that table)
I know how for example sobel, canny or log algorithms works and Hough as well. And naturally those algorithms fail at dark or non-contrast images. But is there some another method for example based at "fitting"?
Images showing photo i can get (you see it would be dark) and what i need find:
and this is really "nice" picture (without noise). I tested it on more noise pictures and the result was..simply horrible..
Result of this picture with actual algorithm log (with another ones it looks same):
I know image and edge recognition is not simple challenge but are there some new better methods or something like that what i can try to use?
In one of posts in here i found LSD algorithm. It seems very nice descripted and it seems it is recognizing really nice straight lines as well. Do you think it would be better to use it insted of the canny or sobel detection?
Another solution will be corner detection, on my sample images it works better but it recognize too much points and there will problem with time..i will need to connect all the points and "find" the table..
Another solution:
I thought about point to point mapping. That i will have some "virtual" table and try to map that table above with that "virtual" table (simple 2d square in painting :] )..But i think point to point mapping will give me big errors or it will not working.
Does someone have any advice what algorithm use to?
I tried recognize edges in FIJI and then put the edge detected image in matlab, but with hough it works bad as well..:/..
What do you think it would be best to use? In short i need find some algorithm working on non contrast, dark images.
I'd try some modified snakes algorithm:
you parameterize your rectangle with 4 points and initialize them somewhere in the image corners. Then you move the points towards image features using some optimization algorithm (e.g. gradient descent, simulated annealing, etc.).
The image features could be a combination of edge features (e.g. sobel directly or sobel of some gaussian filtered image) to be evaluated on the lines between those four points and corner features to be evaluated at those 4 points.
Additionally you can penalize unlikely rectangles (maybe depending on the angles between the points or on the distance to the image boundary).
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.
I have two images. In one of the images, my eye is in the center position and in the other image, it is in the left. How do I find out whether my eye is in the left or the right?
I am using MATLAB. Are there any functions for this?
A simple solution is to try to detect the iris using circular Hough Transform.
You can find a lot materials out there. To name a few, these two fileexchange submissions:
Hough Transform for circle
detection
Circle Detection via Standard Hough
Transform
This sounds like Eye tracking implemented in MATLAB which is a fairly popular research topic.
If you want a more detailed answer, please answer the following questions:
Do you know the coordinates of your eye in the first image?
What kind of motion is there between the two images? Rotation/translation/scaling/...?
Do you want this to be real-time?
What is the resolution of the images?
Are there going to be more eyes in the image apart from yours?
If you are willing to select the eye in one image you can use template matching to find it in others (for example you can mark it in the first frame of a video and then find it in all other frames).
Look at the normxcor2 function in matlab:
http://www.nd.edu/~hpcc/solaris8_usr_local/src/matlab6.1/help/toolbox/images/normxcorr2.html
This technique is robust to constant illumination change, but will fail if the appearance of the eye changes significantly between the image you took the template from and the image you are searching in.
If you are going to search for the eye in a lot of frames (for example, eye tracking from a webcam) then you should look at stronger techniques such as the Kalman Filter or the Particle Filter (aka Condensation Filter in computer vision)
By using Color Distance Maps, the skin and non skin area can be differentiated and thus the non skin area contains the iris. From the iris, the whole eye could be detected. Hope it works.
You should also have a look at Eye Ball Detection in MATLAB , they have detected eyes first and then detected the EyeBall.