I am new to image processing. I want to find the surface between black and white pixels which separates them. Here is the link of image.
The size of image is (21,900,900)
https://drive.google.com/file/d/1zUWK0Fb_n6f1JZou5mrUJq0x3h2X8mBK/view?usp=sharing
I tried to use boundarymask command of MATLAB in one plane of image but I am getting noise and also it works for 2d image only. Please suggest me how to find boundary 3d surface here. Thank you.
This is the output image after applying boundarymask.
Your first step should be to get rid of your noise. Since you got some kind of salt and pepper noise you can to that using the median filter on a 2D-image with medfilt2() in matlab. After that you can use an edge ditector to find your edge pixels. The code for this could look like this. If you want the surface, you need to loop this, over the 3rd dimension of your 3D-image. The code will look like this:
for ii=1:16
I=imread('image.tif',ii);
I_bs=boundarymask(I);
I_filt=medfilt2(I_bs,[7 7]);
boundarysurface(:,:,ii)=edge(I_filt,'Canny');
end
The edge detector I used here is certainly overkill for this easy case, but was the easiest thing I could think of in short term. If performance is relevant let me know, and I will give you another approach.
Related
I would like to follow a curve (with matlab or opencv) and to find the other end of it when it is cut by an empty space like this example, which is simplified to illustrate the problem:
Link to image of cut curve
Real images are more like this one: Link to real image to analyse
To follow the curve, I can use a skeleton and look at the neighbourhood. The problem is that I don't know how to find the other end efficiently.
I don't think that closing or opening operations could help because as shown on the previous image, there are other curves and the two parts of the curve are quite far from each other so it could lead to boundaries between the different curves instead of the two parts.
I was thinking about polynomial evaluation which could be a solution for simple curves but I am not sure about the precision I could get. If I use a skeleton, I have to find exactly the right pixel or to search in a reasonable neighbourhood which would take some time and once again, as there are other curves in the images, I have to be sure that I will find the good one.
That's why I am searching for an existing function which could estimate precisely the trajectory of the curve and give an usefull output to go further and find the second part of the curve.
If that kind of function doesn't exist, I'm open to any other way of analysing the problem if it can help.
I will start to explain with the first image you provided, you can implement common OpenCV function useful for detecting contour(black region in your case as you have binary image) known as cv2.findContours(), which returns the coordinates of the edges of the surface detected then you can plot each detected contour separately in a blank image to get the edge of your desired line.
Now coming to your 2nd image you have to be slightly careful while performing above analysis as there are many tiny lines. get back to me for further help
I have this image:
and would like to get the points on the two black layers to calculate the area siz between the 2 layers.
What is the best way to proceed here? I was thinking to apply dijsktra algorithm or hough transform but it did not really work out.
I have some similar pictures to detect parabola.However, I only need to calculate the number. When it comes to your problem, I think you can use functions such as 'bwconncomp',and calculate the area according to the two edges.
I want to detect the contour of a ring/disc which may be rotated in 3D. I used Detect circles with various radii in grayscale image via Hough Transform by Tao Peng. The results were very close to my requirement. Except for two points:
Using Tao Peng's code I could get a neat blue line indicating the detected contour. I want to access these co-ordinates (sub-pixels) for further processing. I could not figure out where these co-ordinates of detected contour are stored. If you have any idea, please let me know.
Is there any such code to detect ellipse and not only circles? This is because a ring when rotated in 3D wouldn't necessarily be a circle (in which case Tao Peng's code fails. But this is the best I have come across till date. I do not want to binarize my image, as I'll be losing out on a lot of information). Do let me know if there's anything.
Apart from this code, if there's any other one which does a similar job (even if it is like Tao Peng's code, for circles, plus gives me the co-ordinate values), please tell me.
I would prefer MATLAB, but C would also do.
This is an example image who's contour I want to detect, with high accuracy. (The outline of silver disc)
Regards.
Edit:
Here is an example of my output using Tao Peng's code.
I am developing a project of detecting vehicles' headlights in night scene. First I am working on a demo on MATLAB. My detection method is edge detection using Difference of Gaussian (DoG): I take the convolution of the image with Gaussian blur with 2 difference sigma then minus 2 filtered images to find edge. My result is shown below:
Now my problem is to find a method in MATLAB to circle the round edge such as car's headlights and even street lights and ignore other edge. If you guys got any suggestion, please tell me.
I think you may be able to get a better segmentation using a slightly different approach.
There is already strong contrast between the lights and the background, so you can take advantage of this to segment out the bright spots using a simple threshold, then you can apply some blob detection to filter out any small blobs (e.g. streetlights). Then you can proceed from there with contour detection, Hough circles, etc. until you find the objects of interest.
As an example, I took your source image and did the following:
Convert to 8-bit greyscale
Apply Gaussian blur
Threshold
This is a section of the source image:
And this is the thresholded overlay:
Perhaps this type of approach is worth exploring further. Please comment to let me know what you think.
I've scanned an old photo with paper texture pattern and I would like to remove the texture as much as possible without lowering the image quality. Is there a way, probably using Image Processing toolbox in MATLAB?
I've tried to apply FFT transformation (using Photoshop plugin), but I couldn't find any clear white spots to be paint over. Probably the pattern is not so regular for this method?
You can see the sample below. If you need the full image I can upload it somewhere.
Unfortunately, you're pretty much stuck in the spatial domain, as the pattern isn't really repetitive enough for Fourier analysis to be of use.
As #Jonas and #michid have pointed out, filtering will help you with a problem like this. With filtering, you face a trade-off between the amount of detail you want to keep and the amount of noise (or unwanted image components) you want to remove. For example, the median filter used by #Jonas removes the paper texture completely (even the round scratch near the bottom edge of the image) but it also removes all texture within the eyes, hair, face and background (although we don't really care about the background so much, it's the foreground that matters). You'll also see a slight decrease in image contrast, which is usually undesirable. This gives the image an artificial look.
Here's how I would handle this problem:
Detect the paper texture pattern:
Apply Gaussian blur to the image (use a large kernel to make sure that all the paper texture information is destroyed
Calculate the image difference between the blurred and original images
EDIT 2 Apply Gaussian blur to the difference image (use a small 3x3 kernel)
Threshold the above pattern using an empirically-determined threshold. This yields a binary image that can be used as a mask.
Use median filtering (as mentioned by #Jonas) to replace only the parts of the image that correspond to the paper pattern.
Paper texture pattern (before thresholding):
You want as little actual image information to be present in the above image. You'll see that you can very faintly make out the edge of the face (this isn't good, but it's the best I have time for). You also want this paper texture image to be as even as possible (so that thresholding gives equal results across the image). Again, the right hand side of the image above is slightly darker, meaning that thresholding it well will be difficult.
Final image:
The result isn't perfect, but it has completely removed the highly-visible paper texture pattern while preserving more high-frequency content than the simpler filtering approaches.
EDIT
The filled-in areas are typically plain-colored and thus stand out a bit if you look at the image very closely. You could also try adding some low-strength zero-mean Gaussian noise to the filled-in areas to make them look more realistic. You'd have to pick the noise variance to match the background. Determining it empirically may be good enough.
Here's the processed image with the noise added:
Note that the parts where the paper pattern was removed are more difficult to see because the added Gaussian noise is masking them. I used the same Gaussian distribution for the entire image but if you want to be more sophisticated you can use different distributions for the face, background, etc.
A median filter can help you a bit:
img = imread('http://i.stack.imgur.com/JzJMS.jpg');
%# convert rgb to grayscale
img = rgb2gray(img);
%# apply median filter
fimg = medfilt2(img,[15 15]);
%# show
imshow(fimg,[])
Note that you may want to pad the image first to avoid edge effects.
EDIT: A smaller filter kernel than [15 15] will preserve image texture better, but will leave more visible traces of the filtering.
Well i have tried out a different approach using Anisotropc diffusion using the 2nd coefficient that operates on wider areas
Here is the output i got:
From what i can See from the Picture, the Noise has a relatively high Frequency Compared to the image itself. So applying a low Pass filter should work. Have a look at the Power spectrum abs(fft(...)) to determine the cutoff Frequency.