I am trying to create a model of background from multiple images of the same size. I want to use the model to segment moving objects from the roller belt (background model), but I'm not sure how to achieve this.
My concern is how to compute background model with 4 images representing roller belt?
I was thinking of calculating mean for each belt image, add it together, and divide it by the number of images, but in this case I will end up with one value for the whole background. How can I compute the mean of each pixel by looking at 4 images provided?
%some example data
A{1}=imread('http://dummyimage.com/600x400/00a/fff.jpg&text=a');
A{2}=imread('http://dummyimage.com/600x400/00a/fff.jpg&text=b');
A{3}=imread('http://dummyimage.com/600x400/00a/fff.jpg&text=c');
A{4}=imread('http://dummyimage.com/600x400/00a/fff.jpg&text=d');
%make sure every image is of type double. Every uint type is possible as well, but requires casting at the end.
for ix=1:numel(A),A{ix}=im2double(A{ix});end
%concatinate on ndims+1, which means 4th dimension for color images and third dimension for grey scale images.
I=mean(cat(ndims(A{1})+1,A{:}),ndims(A{1})+1);
imshow(I);
If the images are im1,im2,im3,im4 calculate the mean along the third dimension.
% concatenate the images to a single 3-D stack.
im = cat(3,im1,im2,im3,im4);
% find the mean of each pixel.
meanIm = mean(im,3);
Related
I have two binary images, each of which have a single white filled parallelogram and a black background. The only difference between the two images is that the parallelograms are in different locations and are slightly different from one another in shape. All the parameters between the two images are the same except for that one change.
I want to check how similar the shape of the two parallelograms are, by using some sort of comparing measure.
I looked into ssimval function in MATLAB but it seems to be taking the whole image into consideration rather than just the white blobs. Is there any other function I can use for this purpose?
For visually checking similarity, you can plot their probability density function and for numeric similarity, compute some similarity measure, such as, KL Divergence, etc.
In a simple way, you can segment your binary image with simple bwlabel function. Then use regionprops function to find perimeter and area of your desire segment. Moreover, center of region is also another comparison point.
You could do it with polygons, by using the polyshape class.
First convert the binary mask to a set of corner points. You can do it with a convex hull, by calling regionprops(bwI, 'ConvexHull').
Then convert the corner points into polygons, by calling polyshape.
Finally measure the dissimiliarities of the polygons by measuring their turning distance. Turning distance is rotation- and scaling invariant, so you might want to add additive terms to your distance metric for those if your problem demands it.
A very simple solution for comparing two binary image is using boolean operations.
Your images contains zero and one values. so If you use boolean operation.
suppose your two images are : B1 , B2
C = B1 & (~B2)
if sum(C(:))==0
% two image are same
else
% two image are different
end
I'm an undergrad student working in a cell biology lab with a basic background in matlab. I'm working on a project of tracking cell trajectory (time lapse) on a petri dish. Below are two example images that i used the watershed feature to separate from the background. The original pictures had neon green cells, now this is all in black and white/
Let's say i have 20 pictures like this, how might I superimpose one on top of another so they all of equal transparency?
Then, how can i add a colormap that represents time? (The bottom most picture is one end of the colormap and the most recent picture is the opposite end) <- this is extremely challenging as it often things the background is black and not NaN
The Basic Idea
Probably the easiest way to do this, is to take the binary image for each layer, and multiply the image by the time at which it was acquired (or it's index in time). Then you can concatenate all images along the third dimension (using cat). You can compute the maximum value along the third dimension using max. This will make the newer time points appear to be "on top" of the older time points. You can then display the resulting flattened matrix using imagesc and it will automatically map to the colormap for the current figure. Typically we would refer to this as a maximum intensity projection.
Creating Some Data
First since you've only provided two images, I'm going to create some shifted versions of the first image you've provided for the demonstration.
% Create some pseudo-data in a cell array that represents the image over time
im = imread('http://i.imgur.com/xTurvfO.jpg');
im = im(:,:,1);
ims = cell(1, 5);
% Create some shifted versions of im1
shifts = round(linspace(0,1000,5));
for k = 1:numel(shifts)
ims{k} = circshift(im > 100, shifts([k k]));
end
Implementing the Method
Now for the application of the method I discussed
% For each image, multiply the binary mask by the time
for k = 1:numel(ims)
ims{k} = ims{k} * k;
end
% Concatenate all images along the third dimension
IMS = cat(3, ims{:});
% Flatten by taking the maximum value along the third dimension
MIP = max(IMS, [], 3);
% Display the resulting flattened image using imagesc
imagesc(MIP);
% Create a custom colormap with black at the end to create our black background
colormap(cat(1, [0 0 0], parula))
The Result
I have used imfuse to create composite images, which is similar to combining multiple channels on a fluorescent microscope. The Mathworks documentation is http://www.mathworks.com/help/images/ref/imfuse.html.
The tricky part is choosing the vector for color channels. For example, [2,1,2] means choosing B(lue) for image 1, R(ed) and G(reen) for image 2. [2,1,2] is the scheme recommended for colorblind people and gives figure on the left of this image. Using [1,0,2] for red/blue gives the the figure on the right.
fig1 = imread([basepath filesep 'fig.jpg']); %white --> black
fig2 = imread([basepath filesep 'fig2.jpg']);
fig_overlay = imfuse(fig1, fig2,'falsecolor','Scaling','joint', 'ColorChannels', [1,0,2]);
imshow(fig_overlay)
I'm working with MATLAB2014b and I have to find out the corresponding points of my region of interest which in my case are multiple objects in stereo images which are white in color but no specific shape (irregular in shape). I will used these corresponding points to find out the X, Y and Z coordinates by using triangulate function. By using color segmentation on each image separately, I found out points but they are not corresponds to each other. Also, as number of white objects in both images may vary by one or two more or less objects in either image, so I need to remove those extra coordinates automatically. How can I proceed to solve this?
Thanks.
I was working on my image processing problem with detecting coins.
I have some images like this one here:
and wanted to separate the falsely connected coins.
We already tried the watershed method as stated on the MATLAB-Homepage:
the-watershed-transform-strategies-for-image-segmentation.html
especially since the first example is exactly our problem.
But instead we get a somehow very messed up separation as you can see here:
We already extracted the area of the coin using the regionprops Extrema parameter and casting the watershed only on the needed area.
I'd appreciate any help with the problem or even another method of getting it separated.
If you have the Image Processing Toolbox, I can also suggest the Circular Hough Transform through imfindcircles. However, this requires at least version R2012a, so if you don't have it, this won't work.
For the sake of completeness, I'll assume you have it. This is a good method if you want to leave the image untouched. If you don't know what the Hough Transform is, it is a method for finding straight lines in an image. The circular Hough Transform is a special case that aims to find circles in the image.
The added advantage of the circular Hough Transform is that it is able to detect partial circles in an image. This means that those regions in your image that are connected, we can detect them as separate circles. How you'd call imfindcircles is in the following fashion:
[centers,radii] = imfindcircles(A, radiusRange);
A would be your binary image of objects, and radiusRange is a two-element array that specifies the minimum and maximum radii of the circles you want to detect in your image. The outputs are:
centers: A N x 2 array that tells you the (x,y) co-ordinates of each centre of a circle that is detected in the image - x being the column and y being the row.
radii: For each corresponding centre detected, this also gives the radius of each circle detected. This is a N x 1 array.
There are additional parameters to imfindcircles that you may find useful, such as the Sensitivity. A higher sensitivity means that it is able to detect circular shapes that are more non-uniform, such as what you are showing in your image. They aren't perfect circles, but they are round shapes. The default sensitivity is 0.85. I set it to 0.9 to get good results. Also, playing around with your image, I found that the radii ranged from 50 pixels to 150 pixels. Therefore, I did this:
im = im2bw(imread('http://dennlinger.bplaced.net/t06-4.jpg'));
[centers,radii] = imfindcircles(im, [50 150], 'Sensitivity', 0.9);
The first line of code reads in your image directly from StackOverflow. I also convert this to logical or true black and white as the image you uploaded is of type uint8. This image is stored in im. Next, we call imfindcircles in the method that we described.
Now, if we want to visualize the detected circles, simply use imshow to show your image, then use the viscircles to draw the circles in the image.
imshow(im);
viscircles(centers, radii, 'DrawBackgroundCircle', false);
viscircles by default draws the circles with a white background over the contour. I want to disable this because your image has white circles and I don't want to show false contouring. This is what I get with the above code:
Therefore, what you can take away from this is the centers and radii variables. centers will give you the centre of each detected circle while radii will tell you what the radii is for each circle.
Now, if you want to simulate what regionprops is doing, we can iterate through all of the detected circles and physically draw them onto a 2D map where each circle would be labeled by an ID number. As such, we can do something like this:
[X,Y] = meshgrid(1:size(im,2), 1:size(im,1));
IDs = zeros(size(im));
for idx = 1 : numel(radii)
r = radii(idx);
cen = centers(idx,:);
loc = (X - cen(1)).^2 + (Y - cen(2)).^2 <= r^2;
IDs(loc) = idx;
end
We first define a rectangular grid of points using meshgrid and initialize an IDs array of all zeroes that is the same size as the image. Next, for each pair of radii and centres for each circle, we define a circle that is centered at this point that extends out for the given radius. We then use these as locations into the IDs array and set it to a unique ID for that particular circle. The result of IDs will be that which resembles the output of bwlabel. As such, if you want to extract the locations of where the idx circle is, you would do:
cir = IDs == idx;
For demonstration purposes, this is what the IDs array looks like once we scale the IDs such that it fits within a [0-255] range for visibility:
imshow(IDs, []);
Therefore, each shaded circle of a different shade of gray denotes a unique circle that was detected with imfindcircles.
However, the shades of gray are probably a bit ambiguous for certain coins as this blends into the background. Another way that we could visualize this is to apply a different colour map to the IDs array. We can try using the cool colour map, with the total number of colours to be the number of unique circles + 1 for the background. Therefore, we can do something like this:
cmap = cool(numel(radii) + 1);
RGB = ind2rgb(IDs, cmap);
imshow(RGB);
The above code will create a colour map such that each circle gets mapped to a unique colour in the cool colour map. The next line applies a mapping where each ID gets associated with a colour with ind2rgb and we finally show the image.
This is what we get:
Edit: the following solution is more adequate to scenarios where one does not require fitting the exact circumferences, although simple heuristics could be used to approximate the radii of the coins in the original image based on the centers found in the eroded one.
Assuming you have access to the Image Processing toolbox, try imerode on your original black and white image. It will apply an erosion morphological operator to your image. In fact, the Matlab webpage with the documentation of that function has an example strikingly similar to your problem/image and they use a disk structure.
Run the following code (based on the example linked above) assuming the image you submitted is called ima.jpg and is local to the code:
ima=imread('ima.jpg');
se = strel('disk',50);
eroded = imerode(ima,se);
imshow(eroded)
and you will see the image that follows as output. After you do this, you can use bwlabel to label the connected components and compute whatever properties you may want, for example, count the number of coins or detect their centers.
I have an image that was read in using the imread function. My goal is to collect pairs of pixels in an image in MATLAB. Specifically, I have read a paper, and I am trying to recreate the following scenario:
First, the original image is grouped into pairs of pixel values. A pair consists of two neighboring pixel values or two with a small difference value. The pairing could be done horizontally by pairing the pixels on the same row and consecutive columns, or vertically, or by a key-based specific pattern. The pairing could be through all pixels of the image or just a portion of it.
I am looking to recreate the horizontal pairing scenario. I'm not quite sure how I would do this in MATLAB.
Assuming your image is grayscale, we can easily generate a 2D grid of co-ordinates using ndgrid. We can use these to create one grid, then shift the horizontal co-ordinates to the right to make another grid and then use sub2ind to convert the 2D grid into linear indices. We can finally use these linear indices to create our pixel pairings that you have described in your comments (you should really add that to your post BTW). What's important is that you need to skip over every other column in a row to ensure unique pixel pairings.
I'm also going to assume that your image is grayscale. If we go to colour, this will be slightly more complicated, and I'll leave that to you as a learning exercise. Therefore, assuming your image was read in through imread and is stored in im, do something like this:
[rows,cols] = size(im);
[X,Y] = ndgrid(1:rows,1:2:cols);
ind = sub2ind(size(im), X, Y);
ind_shift = sub2ind(size(im), X, Y+1);
pixels1 = im(ind);
pixels2 = im(ind_shift);
pixels = [pixels1(:) pixels2(:)];
pixels will be a 2D array, where each row gives you the pixel intensities of a particular pairing in the image. Bear in mind that I processed each row independently. As such, as soon as we are done with one row, we simply move on to the next row and continue the procedure. This also assumes that your image has an even number of columns. Should it not, you have a decision to make. You need to either pad the image with one column at the end, and this column can be anything you want, or you can remove this column from the image before processing. If you want to fill in this column, you can either make it all zeroes, or perhaps replicate the last column and place this beside the last column in the original image. Therefore, an appropriate pre-processing step may look something like this:
if mod(cols,2) ~= 0
im = im(:,1:end-1);
end
The above code simply removes the last column in the image if the number of columns is odd. Once you run through this code, you can run the first bit of code that I had above.
Good luck!