how to use filter2

Hi guys

For an image and its associated binary mask, I only want to apply my filter on the pixel whose binary indicator is 1.

How can I do this?

Thanks,

Zhong

Answer 1

[Image Analyst]

Apply the filter to the whole image, then multiply the result by the binary mask. Run this demo:

clc;    % Clear the command window.
clearvars;
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
workspace;  % Make sure the workspace panel is showing.
fontSize = 20;
format compact;

% Read in a standard MATLAB gray scale demo image.
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
baseFileName = 'cameraman.tif';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
  % File doesn't exist -- didn't find it there.  Check the search path for it.
  fullFileName = baseFileName; % No path this time.
  if ~exist(fullFileName, '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.  
% numberOfColorBands should be = 1.
[rows columns numberOfColorBands] = size(grayImage);
% Display the original gray scale image.
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Give a name to the title bar.
set(gcf,'name','Demo by ImageAnalyst','numbertitle','off') 

% Filter it
kernel = ones(11);
kernel = kernel / numel(kernel); % Normalize so mean gray level doesn't change.
filteredImage = imfilter(single(grayImage), kernel);
% Display the filtered image.
subplot(2, 2, 2);
imshow(filteredImage, []);
title('Filtered Image', 'FontSize', fontSize);

% Create a mask (ROI)
mask = false(size(grayImage));
mask(70:180, 110:170) = true;
% Display the filtered image.
subplot(2, 2, 3);
imshow(mask, []);
title('Mask Image', 'FontSize', fontSize);

% Replace the image in the mask area with the filtered version.
maskedFilteredImage = grayImage;
maskedFilteredImage(mask) = filteredImage(mask);
% Display the masked filtered image.
subplot(2, 2, 4);
imshow(maskedFilteredImage, []);
title('Masked Filtered Image', 'FontSize', fontSize);

[zhang]

But my concern is that applying to part of the image is much faster than the whole image

[Sean de Wolski]

Is it that much faster?

And how many times do you have to do this. Don't prematurely optimize.

A = rand(300,300,300)>0.5;

B = rand(300,300,300);

tic

for ii = 1:100

A.*B;

end

toc

%11.8s on my system

Thus it took 11.8 seconds to multiply 100*300^3 elements