object recognition using morphological filters

diffImage = imabsdiff(image1, image2);
bigDifferences = diffImage > 5; % Or whatever.
% Get rid of blobs less than 3 pixels in area
bigDifferences = bwareaopen(bigDifferences, 3); % This is a morphological operation
imshow(bigDifferences, []);

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Image Analyst 2021-1-3

编辑：Image Analyst 2021-1-3

在 MATLAB Online 中打开

So did my code work?

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 22;
%--------------------------------------------------------------------------------------------------------
%    READ IN IMAGE 1
folder = pwd;
baseFileName = 'circuit1.png';
grayImage1 = imread(baseFileName);
% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage1)
if numberOfColorChannels > 1
	% It's not really gray scale like we expected - it's color.
	% Use weighted sum of ALL channels to create a gray scale image.
	grayImage1 = rgb2gray(grayImage1);
	% ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
	% which in a typical snapshot will be the least noisy channel.
	% grayImage = grayImage(:, :, 2); % Take green channel.
end
%--------------------------------------------------------------------------------------------------------
% Display the image.
subplot(2, 2, 1);
imshow(grayImage1);
axis('on', 'image');
title('Image 1', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
set(gca,'ColorScale','log')
%--------------------------------------------------------------------------------------------------------
%    READ IN IMAGE 2
folder = pwd;
baseFileName = 'circuit2.png';
grayImage2 = imread(baseFileName);
% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage2)
if numberOfColorChannels > 1
	% It's not really gray scale like we expected - it's color.
	% Use weighted sum of ALL channels to create a gray scale image.
	grayImage2 = rgb2gray(grayImage2);
	% ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
	% which in a typical snapshot will be the least noisy channel.
	% grayImage = grayImage(:, :, 2); % Take green channel.
end
%--------------------------------------------------------------------------------------------------------
% Display the image with the original, linear colormap.
subplot(2, 2, 2);
imshow(grayImage2);
axis('on', 'image');
title('Image 2', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
% Make sure they are the same size
if ~isequal(size(grayImage1), size(grayImage2))
	message = 'Error: images are not the same size';
	errordlg(message);
	return;
end
%--------------------------------------------------------------------------------------------------------
% SUBTRACT THE IMAGES
diffImage = imabsdiff(grayImage1, grayImage2);
subplot(2, 2, 3);
imshow(diffImage, []);
axis('on', 'image');
title('Differnce Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
bigDifferences = diffImage > 5; % Or whatever.
% Get rid of blobs less than 3 pixels in area
bigDifferences = bwareaopen(bigDifferences, 3); % This is a morphological operation
subplot(2, 2, 4);
imshow(bigDifferences, []);
axis('on', 'image');
title('Big Differences', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
%--------------------------------------------------------------------------------------------------------
% Measure area and locations
props = regionprops(bigDifferences, 'Area', 'Centroid');
allAreas = [props.Area]
xy = vertcat(props.Centroid) % get (x,y) of each centroid.

Remi MOrre 2021-1-5

编辑：Remi MOrre 2021-1-5

Ok thanks man you were very helpful. by the way I was able to detect the objects by applyaing erosion followed by thresholding fowllowed by opening the image.

Image Analyst 2021-1-5

Yes, like I said, if your definition of an object is something bright, you can find it by thresholding. You can find a full demo in my Image Segmentation Tutorial in my File Exchange.

An erosion does a local minimum operation, so that will make things darker and shrink the bright blobs, possibly making the smaller blobs disappear so that only larger bright blobs remain. Then you thresholded which gave you a binary image of those larger bright blobs. An opening is an erosion followed by a dilation so that makes the binary blobs smaller and then enlarges them again to their original size, roughly. What it does is to smooth out the black parts of the image by getting rid of the small blobs and snipping off any "tendrils" or "peninsulas" that may poke out of the bright blobs. It may potentially separate blobs into more blobs so you may want to call bwareaopen() or bwareafilt() after that. I'm not sure the erosion at the very beginning is really necessary, but whatever -- as long as it works and you're happy with the result.

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object recognition using morphological filters

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