MATLAB: Image Edge Detection Using Edge Function

image processingImage Processing ToolboxMATLAB

I would like to detect edges in the image in the attached TestImage.jpg. I've highlighted my region of interest in (ROI.jpg). I would not like to use a fixed ROI matrix as different images may not always fit the mask. So I would like to do some edge detection to automatically detect my ROI. I've tried the following:

% Read image as gray scale
I = rgb2gray(imread('TestImage.jpg'));
I = im2double(I);
BW = edge(I,'Canny',0.75,sqrt(150));
image(BW,'CDataMapping','scaled')

The results (DetectedEdges.jpg) does not seem to resolve all the edges I require. I've tried different threshold values as well. Is there a better way of getting my ROI?

Best Answer

First I'd get a first shot at the mask by thresholding. Luckily much of the surround is white so thresholding will get the bottom and sides. Then I might try a texture filter, like stdfilt(), because you know your object of interest is rough while the curved bars/rods behind it are smooth. Then threshold that texture mask and AND it with your intensity thresholding mask. If that doesn't work, post the code for it and we can start tweaking it.

Related Solutions

MATLAB: Obtain average image threshold without the background

Try this:

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
folder = pwd;
baseFileName = 'image.png';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
	% The file doesn't exist -- didn't find it there in that folder.
	% Check the entire search path (other folders) for the file by stripping off the folder.
	fullFileNameOnSearchPath = baseFileName; % No path this time.
	if ~exist(fullFileNameOnSearchPath, 'file')
		% Still didn't find it.  Alert user.
		errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
		uiwait(warndlg(errorMessage));
		return;
	end
end
grayImage = imread(fullFileName);
% 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(grayImage);
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.
	grayImage = rgb2gray(grayImage);
	% 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(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
%--------------------------------------------------------------------------------------------------------
%    SEGMENTATION OF IMAGE
% Get a binary image of just the blob and not including the black background or the white surround.
mask = grayImage > 0 & grayImage < 255;  %imbinarize(grayImage);
subplot(2, 2, 2);
imshow(mask, []);
impixelinfo;
title('Initial Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
% Display histogram of image but only the image within the mask
subplot(2, 3, 4);
histogram(grayImage(mask), 0:255);
grid on;
title('Histogram of gray image', 'FontSize', fontSize);
% Now get the threshold
thresh = 255 * graythresh(grayImage(mask))
% Put a red line there at the threshold
xline(thresh, 'Color', 'r', 'LineWidth', 2);
% Get a binary image of the bright stuff
brightStuff = grayImage >= thresh & mask;
subplot(2, 3, 5);
imshow(brightStuff, []);
impixelinfo;
title('Bright Pixels in Region', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
subplot(2, 3, 6);
darkStuff = ~brightStuff & mask;
imshow(darkStuff, []);
impixelinfo;
title('Dark Pixels in Region', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;

MATLAB: Remove parts from Image and Fill Parts

Pretty easy. Threshold, fill holes, take the 3 largest.

Solution below. Adapt as needed.

% Initialization steps.
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 = 14;
filename = fullfile(pwd, 'image_1.jpg');
grayImage = imread(filename);
if ndims(grayImage) == 3
	% It's really an RGB image, not a gray scale image.  
	% Convert to gray scale
	grayImage = rgb2gray(grayImage);
end
subplot(2, 3, 1);
imshow(grayImage, []);
title('Original Image', 'FontSize', fontSize);
% Threshold
subplot(2, 3, 2);
imhist(grayImage);
grid on;
title('Histogram', 'FontSize', fontSize);
mask = grayImage < 128; % or whatever value works
subplot(2, 3, 3);
imshow(mask, []);
title('Initial Binary Image', 'FontSize', fontSize);
% Fill Holes
mask = imfill(mask, 'holes');
subplot(2, 3, 4);
imshow(mask, []);
title('After holes filled', 'FontSize', fontSize);
% Take 3 largest blobs then invert intensity.
mask = ~bwareafilt(mask, 3);
subplot(2, 3, 5);
imshow(mask, []);
title('Final Image Showing 3 Largest', 'FontSize', fontSize);

Best Answer

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MATLAB: Obtain average image threshold without the background

MATLAB: Remove parts from Image and Fill Parts

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