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From: Image Analyst on 7 May 2008 14:27
"William Thielicke" <w.thielicke.doesntLikeSpam.(a)rug.nl>
wrote in message <fvsnv0$k78$1(a)fred.mathworks.com>...
> Hey!
> I am also new, but can't you use A=imread('...') and then
> B=A(1:5 1)
>
> Oder something like that (in a correct syntax.... ;-D)
> Cheers,
> William
-----------------------------------------------------------
-----------------------------------------------
No, William - that won't work. What you need to do Philip
is to first find the objects in your image. There are
many ways to do this but basically you want to get your
color image down to a point where you can threshold it to
form a binary image. Then you do bwlabel() on it,
followed by regionprops(). The structure returned by
regionprops will have information (such as size, shape,
coordinates of pixels, etc.) in it for every object in
your image. You can then do whatever you want to do with
the information (e.g. make distributions or calculate
means). If you want to erase them from the image you can
find the indexes of the objects you want to erase and then
zero out the pixels from the labelled image. Then you
could threshold the labelled image at a value of 1 to form
a binary mask image, which you can then multiply by your
original image to zero out those parts of your image not
meeting your criteria (such as their area is more than 5
pixels or its shape is not rectangular, etc.) It might
sound more complicated than it really is. Once you have
the image ready for thresholding, the rest of it is only
like 8 or 9 lines of code.
Regards,
ImageAnalyst

Here's some expanded demo code for you that I wrote:

disp(' ');
disp('Running BlobsDemo.m...');
originalImage = imread('coins.png'); % Read in image
binaryImage = im2bw(originalImage, 0.4); % Threshold
to binary

subplot(3,2,1); imagesc(originalImage); colormap(gray
(256)); title('Original Image');
subplot(3,2,2); imagesc(binaryImage); colormap(gray(256));
title('Binary Image');

labeledImage = bwlabel(binaryImage, 8); % Label each
blob so can do calc on it
coloredLabels = label2rgb
(labeledImage, 'hsv', 'k', 'shuffle'); % pseudo random
color labels

subplot(3,2,3); imagesc(labeledImage); title('Labeled
Image');
subplot(3,2,4); imagesc(coloredLabels); title('Pseudo
colored labels');

blobMeasurements = regionprops(labeledImage, 'all'); %
Get all the blob properties.
numberOfBlobs = size(blobMeasurements, 1);

% bwboundaries returns a cell array, where each cell
% contains the row/column coordinates for an object in the
image.
% Plot the borders of all the coins on the original
% grayscale image using the coordinates returned by
bwboundaries.
subplot(3,2,5); imagesc(originalImage); title('Outlines');
hold on;
boundaries = bwboundaries(binaryImage);
for k = 1 : numberOfBlobs
thisBoundary = boundaries{k};
plot(thisBoundary(:,2), thisBoundary
(:,1), 'g', 'LineWidth', 2);
end
hold off;

fprintf(1,'Blob # Mean Intensity Area Perimeter
Centroid\n');
for k = 1 : numberOfBlobs % Loop through all
blobs.
% Find the mean of each blob. (R2008a has a
better way where you can pass the original image
% directly into regionprops. The way below works
for all versions including earlier versions.)
thisBlobsPixels = blobMeasurements(k).PixelIdxList; %
Get list of pixels in current blob.
meanGL = mean(originalImage
(thisBlobsPixels)); % Find mean intensity (in
original image!)
blobArea = blobMeasurements(k).Area; %
Get area.
blobPerimeter = blobMeasurements(k).Perimeter;
% Get perimeter.
blobCentroid = blobMeasurements(k).Centroid;
% Get perimeter.
fprintf(1,'#%d %18.1f %11.1f %8.1f %8.1f %8.1f\n', k,
meanGL, blobArea, blobPerimeter, blobCentroid);
end
msgbox('Finished running BlobsDemo.m. Check out the
figure window and the command window for the results.');

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