MATLAB: How to apply confilt

digital image processingfilterimage analysis

I have image of 768 by 1024 pixels, I want to perform an order statistical filter (min and max) on it by employing confilt order with a 5 by 5 window OP = colfilt(IP, [5 5], 'sliding', @min); but I got this error message "??? Error using ==> reshape To RESHAPE the number of elements must not change.

Error in ==> colfilt at 183 b(i*mb+brows,j*nb+bcols) = …"

Best Answer

Don't use colfilt.

To get the local min

minImage = imerode(grayImage, ones(5));

To get the local max

maxImage = imdilate(grayImage, ones(5));

To get the local max-min

rangeImage = rangefilt(grayImage, ones(5));

Related Solutions

MATLAB: Sliding window minimum and maximum filter

If you have the Image Processing Toolbox, you're in luck!. The sliding max filter is called imdilate() and the sliding min filter is imerode(). These are called "morphological operations." No loops needed:

localMinImage = imerode(grayImage, true(3));
localMaxImage = imdilate(grayImage, true(3));

MATLAB: Local enhancement using mean and standard deviation

Here's my demo. It calculates local mean and standard deviation. I leave it up to you to figure out how to enhance the image once you have those images (there are a variety of ways). Someday perhaps I'll upload all my demos to the File Exchange in a "grab bag of image processing demos" but until then here it is:

% Demo to take the local mean, variance, and standard deviation
% of a gray scale image.
% userImage, if passed in, is used as the image.
% If userImage is not passed in, user is asked to use a demo image.
% Code written by ImageAnalyst
function local_variance(userImage)
% Clean up.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
workspace;  % Make sure the workspace panel is showing.
% Change the current folder to the folder of this m-file.
% (The line of code below is from Brett Shoelson of The Mathworks.)
% Don't use these lines if you're calling this from another m-file.
if(~isdeployed)
  cd(fileparts(which(mfilename)));
end
% Check that user has the Image Processing Toolbox installed.
hasIPT = license('test', 'image_toolbox');
if ~hasIPT
  % User does not have the toolbox installed.
  message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
  reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
  if strcmpi(reply, 'No')
    % User said No, so exit.
    return;
  end
end
% Initialize.
fontSize = 20;
if nargin == 0
    % No image passed in on the command line.
    % Read in one of the standard MATLAB demo images
    % as our original gray scale image and display it.
    promptMessage = sprintf('Which image do you want to use.\nThe coins or the cameraman?');
    button = questdlg(promptMessage, 'Select Image', 'Coins', 'Cameraman', 'Coins');
    if strcmp(button, 'Coins')
        grayImage = double(imread('coins.png')); % Cast to double.


    else
        grayImage = double(imread('cameraman.tif')); % Cast to double.
    end
else
    % Use the image array passed in on the command line.
    grayImage = double(userImage); % Cast to double.
end
% Start timing.
startTime = tic;   
subplot(2, 3, 1);
imshow(grayImage, []);
title('Original Image', 'FontSize', fontSize);
set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
% Blur the image with a 5 by 5 averaging (box filter) window.
blurredImage = conv2(grayImage, ones(5,5)/25);
subplot(2, 3, 2);
imshow(blurredImage, []);
title('Blurred (Local Mean) Image', 'FontSize', fontSize);
% Perform a variance filter.
% Output image is the variance of the input image in a 3 by 3 sliding window.
VarianceFilterFunction = @(x) var(x(:));
varianceImage = nlfilter(grayImage, [3 3], VarianceFilterFunction);
% An alternate way of doing the variance filter is on the next line:
% varianceImage = reshape(std(im2col(originalImage,[3 3],'sliding')), size(originalImage)-2); 
subplot(2, 3, 3);
imshow(varianceImage, [])
title('Variance Image', 'FontSize', fontSize);
% Compute the square root of the variance image to get the standard deviation.
standardDeviationImage = sqrt(varianceImage);
subplot(2, 3, 4);
imshow(standardDeviationImage, [])
title('Standard Deviation Image', 'FontSize', fontSize);
% Compute the standard deviation filter with MATLAB's built-in stdfilt() filter.
standardDeviationImage2 = stdfilt(grayImage);
subplot(2, 3, 5);
imshow(standardDeviationImage2, [])
title('Built-in stdfilt() filter', 'FontSize', fontSize);
% Perform Sobel filter
% h  = fspecial('sobel') returns a 3-by-3 filter h (shown below) that emphasizes horizontal edges 
% using the smoothing effect by approximating a vertical gradient. 
% If you need to emphasize vertical edges, transpose the filter h'.
% [ 1  2  1
%   0  0  0
%  -1 -2 -1 ]
verticalSobelKernel = fspecial('sobel');
sobelImage = imfilter(grayImage, verticalSobelKernel);
subplot(2, 3, 6);
imshow(sobelImage, [])
title('Sobel edge filter', 'FontSize', fontSize);
elapsedTime = toc(startTime);
message = sprintf('Done!\n\nElapsed time = %.2f seconds.', elapsedTime);
msgbox(message);
return; % End of local_variance() function.

Best Answer

Related Solutions

MATLAB: Sliding window minimum and maximum filter

MATLAB: Local enhancement using mean and standard deviation

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