After reading two images (a,b), I want to find any object in "b" image that does not exists in the first image "a" and object could be of any shape does not matter, the two images are pictures captured in the same place with the same state of the camera. But could be some differences, I want to have the number of these different objects.
this what have tried so far
i = imread('camera1.jpg');
j = imread('camera4.jpg');
a = im2double(i)
b = im2double(j)
f1= ones(3,3)/9;
i1=imfilter(i,a);
j1=imfilter(j,b);
ed1 = edge(i1);
ed2 = edge(j1);
madBlock = mean2(abs(double(ed1) - double(ed2)))
I think, the simplest way is to align these two images (e.g. in Hugin) and calculate difference diff = |b-a|.
Next step is thresholding: set to zero all pixels in diff that lower than a threshold. After that do median filtering to omit salt/pepper noice and apply connected components search (marching squares method). You will find differences between images.
Related
I have a binary image that has two connected components. Both are fairly horizontal and one is on the top of the image and the other at the bottom. What I need to do is to extract only the top component which I want to do (or at least what I think is a good method) by taking the component with the lowest y value for the centroid (because MATLAB uses Java to show images, so the origin is at the top left) and erasing the other component. So far I've been able to use regionprops to find which region has the lowest y value for the centroid, but from there I'm not sure how to get a binary image back again with the component I want.
I've read in the documentation that bwconncomp, labelmatrix, and ismember are useful, but I'm not very sure how to use them well (or at all very much).
This is the solution I just made up, but if there's a better or more elegant one I'd love to know about it!
P.S. filtered is my image
connComp = bwconncomp(filtered);
props = regionprops(filtered, 'Centroid');
justTop = zeros(size(filtered,1), size(filtered,2));
if props(1).Centroid(2) > props(2).Centroid(2)
justTop(connComp.PixelIdxList{2}) = 1;
else
justTop(connComp.PixelIdxList{1}) = 1;
end`
I have found a couple areas referring to filling of gaps in binary images in matlab, however I am still struggling. I have written the following code but I cannot get it to work. Here is my binary image:
.
However, what I'm trying to achieve is the following
.
Does anyone know how to do this? I have been trying using imfill but I know I think I need to define boundaries also with the bwlabel function but I dont know how. Any help would be greatly appreciated.
%%Blade_Image_Processing
clc;
clear;
%%Video file information
obj = VideoReader('T9_720p;60p_60mm_f4.MOV');
% Sampling rate - Frames per second
fps = get(obj, 'FrameRate');
dt = 1/fps;
% ----- find image info -----
file_info = get(obj);
image_width = file_info.Width;
image_height = file_info.Height;
% Desired image size
x_range = 1:image_height;
y_range = 1:image_width;
szx = length(x_range);
szy = length(y_range);
%%Get grayscale image
grayscaleimg1 = rgb2gray(read(obj,36));
grayscaleimg = imadjust(grayscaleimg1);
diff_im = medfilt2(grayscaleimg, [3 3]);
t1=60;
t2=170;
range=(diff_im > t1 & diff_im <= t2);
diff_im (range)=255;
diff_im (~range)=0;
% Remove all those pixels less than 300px
diff_im = bwareaopen(diff_im,2000);
%imshow(diff_im)
%imhist(grayscaleimg)
%Fill gaps in binary image
BW2 = imfill(diff_im,'holes');
There are two main problems: desired object has no readily usable distinguishing features, and it touches other object. Second problem could be perhaps cleared with morphological opening/closing (touching object is thin, desired object not, is this always the case?), but first problem remains. If your object touched edge but others didn't or vice versa, you could do something with imfill and subtractions. As it is now, MAYBE something like this would work:
With opening/closing remove connection, so your object is disjoint.
With imfill, remove what is left of this thin horizontal thing.
Then, you can bwlabel and remove everything that touches sides or bottom of the image - in shown case that would leave only your object.
Exact solution depends heavily on what additional constrains are there for your pictures. I believe it is not a one-shot, rather you have more of those pictures and want to correctly find objects on all? You have to check what holds for all pictures, such as if object always touches only something thin or if it always touches only upper edge etc.
I have an image that show some random filled circles (e.g. see here). I want to change these circles to make some irregular shapes. In other words, I want to define a distribution by which I can expand the circles. Clearly, the resulted new objects will not be circles anymore, because the generated objects are expanded based on a distribution which is variable; see this new deformed circle.
I was wondering if there is any method that can do this? In my first try, I tried to use image dilation in Matlab, but I have no idea on how the dilation "distribution" should be used.
IM2 = imdilate(IM,SE)
If you want to do it using dilation, a solution could be:
Let say Im is your original image
ImResult = Same(Im)
ImClone = Clone(Im)
Randomly delete pixels in ImClone. The number of pixels to delete may be a percentage, or whatever you prefer
ImDilate = Dilate(ImClone), with the structuring element of size N
Result = Maximum(Result, ImDilate)
If you want different size of deformations, then you iterate from step 3 to 6, with different structuring element sizes.
But what you want is more an elastic deformation. You should take a look to the free form deformation (FFD).
I have a binary image of several connected components, some large and some small (maybe only 1 pixel). With this I am seeking a way to make each connected component into a checkers pattern, instead of the connected blobs, in an efficient way.
So far I have come up with two ways this could be tried, but they can either produce errors, or be quite unefficient:
I know the entire image and can make a checkers pattern mask to remove 50% of the pixels. This is very fast, but will on average remove 50% of the connected components which are only one pixel in area.
Use bwlabel() in MATLAB/Octave, and loop through each connected component only applying the mask to that component if it is over 1 pixel (while leaving the other components to be considered when the loop gets to them). This can be very inefficient.
Any smart/built-in solutions which could be used?
Code to generate figure
T = zeros(40,40);
T(10:30,10:30) = 1;
chessVec = repmat([1;0],20,1);
T_wanted = (repmat([chessVec circshift(chessVec,1)],1,20).*T);
figure();
subplot(1,2,1);imshow(T);title('Start shape')
subplot(1,2,2);imshow(T_wanted);title('Wanted shape');
Nothing beats blanket checkering for efficiency. All you then need to do is add back the small connected components.
%# create a test image
img = rand(100)>0.8;
img = imclose(img,ones(5));
img = imerode(img,strel('disk',2));
%# get connected components
%# use 4-connect to preserve
%# the diagonal single-pixel lines later
cc = bwconncomp(img,4)
%# create checkerboard using one of Matlab's special matrix functions
chk = invhilb(100,100) < 0;
%# checker original image, add back small stuff
img(chk) = 0;
smallIdx = cellfun(#(x)x<2,cc.PixelIdxList);
img([cc.PixelIdxList{smallIdx}]) = 1;
I have an image containing several specific objects. I would like to detect the positions of those objects in this image. To do that I have some model images containing the objects I would like to detect. These images are well cropped around the object instance I want to detect.
Here is an example:
In this big image,
I would like to detect the object represented in this model image:
Since you originally posted this as a 'gimme-da-codez' question, showing absolutely no effort, I'm not going to give you the code. I will describe the approach in general terms, with hints along the way and it's up to you to figure out the exact code to do it.
Firstly, if you have a template, a larger image, and you want to find instances of that template in the image, always think of cross-correlation. The theory is the same whether you're processing 1D signals (called a matched filter in signal processing) or 2D images.
Cross-correlating an image with a known template gives you a peak wherever the template is an exact match. Look up the function normxcorr2 and understand the example in the documentation.
Once you find the peak, you'll have to account for the offset from the actual location in the original image. The offset is related to the fact that cross-correlating an N point signal with an M point signal results in an N + M -1 point output. This should be clear once you read up on cross-correlation, but you should also look at the example in the doc I mentioned above to get an idea.
Once you do these two, then the rest is trivial and just involves cosmetic dressing up of your result. Here's my result after detecting the object following the above.
Here's a few code hints to get you going. Fill in the rest wherever I have ...
%#read & convert the image
imgCol = imread('http://i.stack.imgur.com/tbnV9.jpg');
imgGray = rgb2gray(img);
obj = rgb2gray(imread('http://i.stack.imgur.com/GkYii.jpg'));
%# cross-correlate and find the offset
corr = normxcorr2(...);
[~,indx] = max(abs(corr(:))); %# Modify for multiple instances (generalize)
[yPeak, xPeak] = ind2sub(...);
corrOffset = [yPeak - ..., xPeak - ...];
%# create a mask
mask = zeros(size(...));
mask(...) = 1;
mask = imdilate(mask,ones(size(...)));
%# plot the above result
h1 = imshow(imgGray);
set(h1,'AlphaData',0.4)
hold on
h2 = imshow(imgCol);
set(h2,'AlphaData',mask)
Here is the answer that I was about to post when the question was closed. I guess it's similar to yoda's answer.
You can try to use normalized cross corelation:
im=rgb2gray(imread('di-5Y01.jpg'));
imObj=rgb2gray(imread('di-FNMJ.jpg'));
score = normxcorr2(imObj,im);
imagesc(score)
The result is: (As you can see, the whitest point corresponds to the position of your object.)
The Mathworks has a classic demo of image registration using the same technique as in #yoda's answer:
Registering an Image Using Normalized Cross-Correlation