I'm working on a fingerprint recognition project but I need to pre-process the image. I go through the following process.
1) Binarization
2) Filtering to removing the "stair-step" effect; i.e smoothing
3) Thin the lines
I'm adding in a step that I'm trying to develop that will fill in any holes that are left after thinning. I'm trying to accomplish this as follows.
4a) Use bwlabel to find regions (I might consider using bwmorp(...,'shrink') to just leave the "blobs" but doing this reduces the size of the blob a little).
4b) Find all regions that do not have the maximum area
4c) Use the location of these regions to shrink these "blobs" to points.
But how can I apply shrink at specified locations?
Binarization
Filtering
Thinning
Hole filling
I am Not sure if I understood the question correctly. But can you take complement of the thinned image and then use bwlabel. After that, count the number of pixels belonging to each label. Apply your criteria to select labels and get their locations. After that, you can use imfill(bw,locations) command.
If you have a region you want to shrink to a point then calculate the center of mass for the region. Do this by just averaging the x and y coordinates of each point in the blob. Set the region pixels to black and the center of mass to white.
The problem that I am having is trimming off the ridges to just leave the filled areas while avoiding erosion of the filled areas (if I were to use erode the ridges would disappear but also reduce the size of the area).
You don't really have to worry about erosion of the small regions as it will have little impact on the center of mass, and you point will still end up going to an appropriate location.
Related
I am currently taking my first steps in the field of computer vision and image processing.
One of the tasks I'm working on is finding the center coordinates of (overlapping and occluded) circles.
Here is a sample image:
Here is another sample image showing two overlapping circles:
Further information about the problem:
Always a monochrome, grayscale image
Rather low resolution images
Radii of the circles are unknown
Number of circles in a given image is unknown
Center of circle is to be determined, preferably with sub-pixel accuracy
Radii do not have to be determined
Relative low overhead of the algorithm is of importance; the processing is supposed to be carried out with real-time camera images
For the first sample image, it is relatively easy to calculate the center of the circle by finding the center of mass. Unfortunately, this is not going to work for the second image.
Things I tried are mainly based on the Circle Hough Transform and the Distance Transform.
The Circle Hough Transform seemed relatively computationally expensive due to the fact that I have no information about the radii and the range of possible radii is large. Furthermore, it seems hard to identify the (appropriate) pixels along the edge because of the low resolution of the image.
As for the Distance Transform, I have trouble identifying the centers of the circles and the fact that the image needs to be binarized implies a certain loss of information.
Now I am looking for viable alternatives to the aforementioned algorithms.
Some more sample images (images like the two samples above are extracted from images like the following):
Just thinking aloud to try and get the ball rolling for you... I would be thinking of a Blob, or Connected Component analysis to separate out your blobs.
Then I would start looking at each blob individually. First thing is to see how square the bounding box is for each blob. If it is pretty square AND the centroid of the blob is central within the square, then you have a single circle. If it is not square, or the centroid is not central, you have more than one circle.
Now I am going to start looking at where the white areas touch the edges of the bounding box for some clues as to where the centres are...
I have an image like this:
What I want to do is to find the outer edge of this cell and the inner edge in the cell between the two parts of different colors.
But this image contains to much detail I think, and is there any way to simplify this image, remove those small edges and find the edges I want?
I have tried the edge function provided by matlab. But it can only find the outer edge and disturbed by those detailed edges.
This is a very challenging work due to the ambiguous boundaries and tiny difference between red and green intensities. If you want to implement the segmentation very precisely and meet some medical requirements, Shai's k-means plus graph cuts may be one of the very few options (EM algorithm may be an alternative). If you have a large database that has many similar images, some machine learning methods might help. Otherwise, I just wrote a very simple code to roughly extract the internal red region for you. The boundary is not that accurate since some of the green regions are also included.
I1=I;
I=rgb2hsv(I);
I=I(:,:,1); % the channel with relatively large margin between green and red
I=I.*(I<0.25);
I=imdilate(I, true(5));
% I=imfill(I,'holes'); depends on what is your definition of the inner boundary
bw=bwconncomp(I);
ar=bw.PixelIdxList;
% find the largest labeled area,
n=0;
for i=1:length(ar)
if length(ar{i})>n
n=length(ar{i});
num=i;
end
end
bw1=bwlabel(I);
bwfinal(:,:,1)=(bw1==num).*double(I1(:,:,1));
bwfinal(:,:,2)=(bw1==num).*double(I1(:,:,2));
bwfinal(:,:,3)=(bw1==num).*double(I1(:,:,3));
bwfinal=uint8(bwfinal);
imshow(bwfinal)
It seems to me you have three dominant colors in the image:
1. blue-ish background (but also present inside cell as "noise")
2. grenn-ish one part of cell
3. red-ish - second part of cell
If these three colors are distinct enough, you may try and segment the image using k-means and Graph cuts.
First stage - use k-means to associate each pixels with one of three dominant colors. Apply k-means to the colors of the image (each pixel is a 3-vector in your chosen color space). Run k-means with k=3, keep for each pixel its distance to centroids.
Second stage - separate cell from background. Do a binary segmentation using graph-cut. The data cost for each pixel is either the distance to the background color (if pixel is labeled "background"), or the minimal distance to the other two colors (if pixel is labeled "foreground"). Use image contrast to set the pair-wise weights for the smoothness term.
Third stage - separate the two parts of the cell. Again do a binary segmentation using graph-cut but this time work only on pixels marked as "cell" in the previous stage. The data term for pixels that the k-means assigned to background but are labeled as cell should be zero for all labels (these are the "noise" pixels inside the cell).
You may find my matlab wrapper for graph-cuts useful for this task.
Consider that I have a colored image like this in which the outline is not complete (There are gaps between lines). I want to be able to fill the area between the lines with one color or another. This actually is a binary image which I got after applying canny edge detector on a corresponding gray scale image.
I tried first dilating the image and then eroding it, but the result is not good enough. I want to be able to preserve the thickness of the root
Any help would be greatly appreciated
Original Image
Image after edge detection and some manual removal of pixels
Using the information in the edge image, I thought I would try to extract pixels from the original image of a certain color. For every white pixel in the edited image, I used a search space in the original image along the same horizontal line. I used different thresholds for R, G and B and I ended up with this
I'm not sure what your original image looks like. It would be helpful to see.
You have gaps between the lines because a line in your original image has two edges, one on each side. The canny algorithm is detecting them both. The Canny edge detection algorithm has at its heart the application of two Sobel kernels to calculate the gradient, one for detecting horizontal edges and one for detection vertical edges.
-1 0 +1
-2 0 +2
-1 0 +1
and
+1 +2 +1
0 0 0
-1 -2 -1
These kernels will present peaks for both sides of the line. One peak positive and one negative. You can exclude one side of the line by excluding the corresponding peak. After taking the gradient of each direction truncate any values below zero (set the values to zero) to remove the second peak. Then continue with the Canny edge detection as usual. This will result in the detection of only a single edge for each line instead of the two that you are seeing now.
I'll add a third approach now that I have seen the image. It looks like most of the information is in the green channel.
Green channel image
This image gives you a decent result if you simply apply a threshold.
Thresholded image with a somewhat arbitrary threshold
You can then either clean this image up by itself or use your edge image. To clean it up with the edge image you produced remove any white pixels that are more than a certain distance from one of your detected edges (create a Euclidean distance map from your edge image and use that to set any white pixels greater than a certain distance from an edge to black).
If you are still collecting images you may want to try to position the camera in a way to avoid the bottom of the jar (or whatever this is).
You could attempt to use a line scanning methodology. Start at the side and scan horizontally. When you hit an edge you assume you are in a root and you start setting the voxels to white. When you hit another edge you assume you are leaving a root and you start. There will be some fringe cases and you may want to add additional checks, such as limiting the allowed thickness of a root.
You could also do a flood fill style algorithm where you take a seed point in a root and travel up the root filling it in.
Not sure how well these would work as it depends on the image and I did not test it.
i need to find the distance between the two points.I can find the distance between them manually by the pixel to cm converter in the image processing tool box. But i want a code which detects the point positions in the image and calculate the distance.
More accurately speaking the image contains only three points one mid and the other two approximately distanced equally from it...
There might be a better way then this, but I hacked something similar together last night.
Use bwboundaries to find the objects in the image (the contiguous regions in a black/white image).
The second returned matrix, L, is the same image but with the regions numbered. So for the first point, you want to isolate all the pixels related to it,
L2 = (L==1)
Now find the center of that region (for object 1).
x1 = (1:size(L2,2))*sum(L2,1)'/size(L2,2);
y1 = (1:size(L2,1))*sum(L2,2)/size(L2,1);
Repeat that for all the regions in your image. You should have the center of mass of each point. I think that should do it for you, but I haven't tested it.
I am looking for a method that looks for shapes in 3D image in matlab. I don't have a real 3D sample image right now; in fact, my 3D image is actually a set of quantized 2D images.
The figure below is what I am trying to accomplish:
Although the example figure above is a 2D image, please understand that I am trying to do this in 3D. The input shape has these "tentacles", and I have to look for irregular shapes among them. The size of the tentacle from one point to another can change around but at "consistent and smooth" pace - that is it can be big at first, then gradually smaller later. But if suddenly, the shape just gets bigger not so gradually, like the red bottom right area in the figure above, then this is one of the volume of interests. Note that these shapes have more tendency to be rounded and spherical, but some of them are completely arbitrary and random.
I've tried the following methods so far:
Erode n times and dilate n times: given that the "tentacles" are always smaller than the volume of interest, this method will work as long as the volume is not too small. And, we need to have a mechanism to deal with thicker portion of the tentacle that becomes false positive somehow.
Hough Transform: although I have been suggested this method earlier (from Segmenting circle-like shapes out of Binary Image), I see that it works for some of the more rounded shape cases, but at the same time, more difficult cases such that of less-rounded, distorted, and/or arbitrary shapes can slip through this method.
Isosurface: because of my input is a set of 2D quantized images, using an isosurface allow me to reconstruct image in 3D and see things clearer. However, I'm not sure what could be done further in this case.
So can anyone suggests some other techniques for segmenting such shape out of these "tentacles"?
Every point on your image has the property that it is either part of the tentacle, or part of the volume of interest. If it is unknown apriori what the expected girth of the tentacle is, then 1 wont work because we won't be able to set n. However, we know that the n that erases the tentacle is smaller than the n that erases the node. You can for each point replace it with an integer representing the distance to the edge. Effectively, this can be done via successive single pixel erosion, and replacing each pixel with the count of the iteration at which it was erased. Lets call this the thickness at the pixel, but my rusty old mind tells me that there was a term of art for this.
Now we want to search for regions that have a higher-than-typical morphological distance from the boundary. I would do this by first skeletonizing the image (http://www.mathworks.com/help/toolbox/images/ref/bwmorph.html) and then searching for local maxima of the thickness along the skeleton. These are points on the skeleton where the thickness is larger than the neighbor points.
Finally I would sort the local maxima by the thickness, a threshold on which should help to separate the volumes of interest from the false positives.