Census Transform is a form of non-parametric local transform (i.e. relies on the relative ordering of local intensity values, and not on the intensity values themselves) used in image processing to map the intensity values of the pixels within a square window to a bit string, thereby capturing the image structure [1]. The centre pixel’s intensity value is replaced by the bit string composed of set of boolean comparisons such that in a square window, moving left to right,
If (CurrentPixelIntensity<CentrePixelIntensity) boolean bit=0
else boolean bit=1
For each comparison the bit is shifted to the left, forming an 8 bit string for a census window of size 3×3 and a 32 bit string for a census window of size 5×5.
Advantages
- Reduces effects of variations caused by camera’s gain and bias.
- Increase in robustness to outliers near depth-discontinuities.
- Encodes local spatial structure.
- Tolerant to factionalism (If a minority of pixels in a local neighborhood has a very different intensity distribution than the majority, only comparisons involving a member of the minority are affected).
- It can distinguish between rotations and reflections
Disadvantages
- Loss of information associated with the pixel.
- As the size of the window increases, so does the requirement of the variable’s size. For a census window of 3×3, the variable used to store the census value would be of size 23, or 8 bits; for a census of window size 5×5 the number of bits required to store census value would be 25 or 32 bits.
Example
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 Census Transform (3×3) |
 Census Transform (5×5) |
Sample Image-ConesMATLAB Code
% *************************************************************************
% Title: Function-Census Transform of a given Image
% Author: Siddhant Ahuja
% Created: May 2008
% Copyright Siddhant Ahuja, 2008
% Inputs: Image (var: inputImage), Window size assuming square window (var:
% windowSize) of 3x3 or 5x5 only.
% Outputs: Census Tranformed Image (var: censusTransformedImage),
% Time taken (var: timeTaken)
% Example Usage of Function: [a,b]=funcCensusOneImage('Img.png', 3)
% *************************************************************************
function [censusTransformedImage, timeTaken] = funcCensusOneImage(inputImage, windowSize)
% Grab the image information (metadata) using the function imfinfo
try
imageInfo=imfinfo(inputImage);
% Since Census Transform is applied on a grayscale image, determine if the
% input image is already in grayscale or color
if(getfield(imageInfo,'ColorType')=='truecolor')
% Read an image using imread function, convert from RGB color space to
% grayscale using rgb2gray function and assign it to variable inputImage
inputImage=rgb2gray(imread(inputImage));
else if(getfield(imageInfo,'ColorType')=='grayscale')
% If the image is already in grayscale, then just read it.
inputImage=imread(inputImage);
else
error('The Color Type of Input Image is not acceptable. Acceptable color types are truecolor or grayscale.');
end
end
catch
inputImage=inputImage;
end
% Find the size (columns and rows) of the image and assign the rows to
% variable nr, and columns to variable nc
[nr,nc] = size(inputImage);
% Check the size of window to see if it is an odd number.
if (mod(windowSize,2)==0)
error('The window size must be an odd number.');
end
if (windowSize==3)
bits=uint8(0);
% Create an image of size nr and nc, fill it with zeros and assign
% it to variable censusTransformedImage of type uint8
censusTransformedImage=uint8(zeros(nr,nc));
else if (windowSize==5)
bits=uint32(0);
% Create an image of size nr and nc, fill it with zeros and assign
% it to variable censusTransformedImage of type uint32
censusTransformedImage=uint32(zeros(nr,nc));
else
error('The size of the window is not acceptable. Just 3x3 and 5x5 windows are acceptable.');
end
end
% Initialize the timer to calculate the time consumed.
tic;
% Find out how many rows and columns are to the left/right/up/down of the
% central pixel
C= (windowSize-1)/2;
for(j=C+1:1:nc-C) % Go through all the columns in an image (minus C at the borders)
for(i=C+1:1:nr-C) % Go through all the rows in an image (minus C at the borders)
census = 0; % Initialize default census to 0
for (a=-C:1:C) % Within the square window, go through all the rows
for (b=-C:1:C) % Within the square window, go through all the columns
if (~(a==0 && b==0)) % Exclude the centre pixel from the calculation
census=bitshift(census,1); %Shift the bits to the left by 1
% If the intensity of the neighboring pixel is less than
% that of the central pixel, then add one to the bit
% string
if (inputImage(i+a,j+b) < inputImage(i,j))
census=census+1;
end
end
end
end
% Assign the census bit string value to the pixel in imgTemp
censusTransformedImage(i,j) = census;
end
end
% Stop the timer to calculate the time consumed.
timeTaken=toc;
In order to run the above code, you should have the image processing toolbox installed in MATLAB.
References
[1] R. Zabih and J. Woodfill, “Non-parametric Local Transforms for Computing Visual Correspondence”, Proceedings of the third European conference on Computer Vision (Vol. II)
