How do I apply a sliding window on an image?

[EDIT: 20110801 10:12 CDT - merge from duplicate - WDR]

pls guide me to use sliding window concept in image processing for finding LBP feature of an image

[Information from other posting]

how to apply a 3*3 sliding window for an image and specify a method to find LBP (local binary pattern) features of an image.

Answer 1

[Sean de Wolski]

doc conv2
doc imfilter
doc blkproc
doc blockproc

a few sliding window tools... Please be more specific if you want more specific guidance.

Answer 2

[Image Analyst]

Here's a blockproc demo for you to study:

function blockproc_demo()
try
  clc; % Clear the command window.
  close all; % Close all figures (except those of imtool.)
  imtool close all; % Close all imtool figures.
  clear; % Erase all existing variables.
  workspace; % Make sure the workspace panel is showing.
  fontSize = 20;

  % Change the current folder to the folder of this m-file.
  if(~isdeployed)
    cd(fileparts(which(mfilename)));
  end

  % Read in standard MATLAB demo image.
  grayImage = imread('cameraman.tif');
  [rows columns numberOfColorChannels] = size(grayImage);
  % Display the original image.
  subplot(2, 2, 1);
  imshow(grayImage, []);
  caption = sprintf('Original Image\n%d by %d pixels', ...
  rows, columns);
  title(caption, 'FontSize', fontSize);
  % Enlarge figure to full screen.
  set(gcf, 'Position', get(0,'Screensize'));
  set(gcf, 'name','Demo by ImageAnalyst', 'numbertitle','off')

  % Block process the image.
  windowSize = 3;
  % Each 3x3 block will get replaced by one value.
  % Output image will be smaller by a factor of windowSize.
  myFilterHandle = @myFilter;
  blockyImage = blockproc(grayImage,[windowSize windowSize], myFilterHandle);
  [rowsP columnsP numberOfColorChannelsP] = size(blockyImage);

  % Display the processed image.
  % It is smaller, but the display routine imshow() replicates
  % the image so that it looks bigger than it really is.
  subplot(2, 2, 2);
  imshow(blockyImage, []);
  caption = sprintf('Image Processed in %d by %d Blocks\n%d by %d pixels\nCustom Box Filter', ...
  windowSize, windowSize, rowsP, columnsP);
  title(caption, 'FontSize', fontSize);

  % Now let's do it an alternate way where we use an anonymous function.
  % We'll take the standard deviation in the blocks.
  windowSize = 8;
  myFilterHandle2 = @(block_struct) ...
  std2(block_struct.data) * ones(size(block_struct.data));
  blockyImageSD = blockproc(grayImage, [windowSize windowSize], myFilterHandle2);
  [rowsSD columnsSD numberOfColorChannelsSD] = size(blockyImageSD);
  subplot(2, 2, 4);
  imshow(blockyImageSD, []);
  caption = sprintf('Image Processed in %d by %d Blocks\n%d by %d pixels\nAnonymous Standard Deviation Filter', ...
  windowSize, windowSize, rowsSD, columnsSD);
  title(caption, 'FontSize', fontSize);

  % Note: the image size of blockyImageSD is 256x256, NOT smaller.
  % Does blockproc behave differently if an anonymous function is used?

  uiwait(msgbox('Done with demo'));
  catch ME
    errorMessage = sprintf('Error in blockproc_demo():\n\nError Message:\n%s', ME.message);
    uiwait(warndlg(errorMessage));
  end
  return;

% Takes one 3x3 block of image data and multiplies it
% element by element by the kernel and
% returns a single value.
function singleValue = myFilter(blockStruct)
  try
    % Assign default value.
    % Will be used near sides of image (due to boundary effects),
    % or in the case of errors, etc.
    singleValue = 0;

    % Create a 2D filter.
    kernel = [0 0.2 0; 0.2 0.2 0.2; 0 0.2 0];
    % kernel = ones(blockStruct.blockSize); % Box filter.

    % Make sure filter size matches image block size.
    if any(blockStruct.blockSize ~= size(kernel))
      % If any of the dimensions don't match.
      % You'll get here near the edges,
      % if the image is not a multiple of the block size.
      % warndlg('block size does not match kernel size');
      return;
    end
    % Size matches if we get here, so we're okay.

    % Extract our block out of the structure.
    array3x3 = blockStruct.data;

    % Do the filtering. Multiply by kernel and sum.
    singleValue = sum(sum(double(array3x3) .* kernel));

  catch ME
    % Some kind of problem...
    errorMessage = sprintf('Error in myFilter():\n\nError Message:\n%s', ME.message);
    % uiwait(warndlg(errorMessage));
    fprintf(1, '%s\n', errorMessage);
  end
  return;

Answer 3

[Caelyn Low]

Histogram of oriented gradient (Hog descriptor)is used to detect the human. First step is to have a detection window of 64x128 pixels, and slide the detection window through the whole input image. So can i use this blockproc function to get the detection window? thanks

[Image Analyst]

Huh? Did you reply to the right question?