Matlab_code Siddhant Ahuja (Sid)

siddhant ahuja (sid)

Compute Variance Map of an Image

i2 Votes

Variance map of an image is calculated by taking a square window of a setsize around a center pixel, and calculating the variance of the values of thepixels.

The variance within the window can be calculated from the followingequation:

(http://siddhantahuja.files.wordpress.com/2009/06/variance-equation.png)

Example

(http://siddhantahuja.files.wordpress.com/2009/06/monopolyleftcolor1.png)Left Image

8 06 2009

1/13/2014 MATLAB code | siddhant ahuja (sid)

wordpress.com/tag/matlab-code/ 1/34

(http://siddhantahuja.files.wordpress.com/2009/06/monopolyleft-variance-map-59.png)Monopoly-Left Image-Variance Map

(Pixels marked as black are low-texture regions)

MATLAB Code

% *************************************************************************

% Title: FunctionCompute Variance map of the image

% Author: Siddhant Ahuja

% Created: May 2008

% Copyright Siddhant Ahuja, 2008

% Inputs: Input Image (var: inputImage), Window Size (var: windowSize),

% Threshold (var: thresh) Typical value is 140

% Outputs: Variance Map (var: varianceImg) , Time taken (var: timeTaken)

% Example Usage of Function: [varianceImg, timeTaken]=funcVarianceMap('MonopolyLeftColor.png', 5, 9);

% *************************************************************************

function [varianceImg, timeTaken]=funcVarianceMap(inputImage, windowSize, thresh)

try

% Grab the image information (metadata) of input image using the function imfinfo

inputImageInfo=imfinfo(inputImage);

if(getfield(inputImageInfo,'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));

% Convert the image from uint8 to double

inputImage=double(inputImage);

else if(getfield(inputImageInfo,'ColorType')=='grayscale'

% If the image is already in grayscale, then just read it.

inputImage=imread(inputImage);


inputImage=double(inputImage);

else

error('The Color Type of Left Image is not acceptable. Acceptable color types are truecolor or grayscale.'

end

end

catch

% if it is not an image but a variable

inputImage=inputImage;



end

% Find the size (columns and rows) of the input 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

% Create an image of size nr and nc, fill it with zeros and assign

% it to variable meanImg

meanImg=zeros(nr, nc);


% it to variable varianceImg

varianceImg=zeros(nr, nc);

% Find out how many rows and columns are to the left/right/up/down of the

% central pixel based on the window size

win=(windowSize1)/2;

tic; % Initialize the timer to calculate the time consumed.

% Compute a map of mean values

for(i=1+win:1:nrwin)

for(j=1+win:1:ncwin)

sum=0.0;

for(a=win:1:win)

for(b=win:1:win)

sum=sum+inputImage(i+a,j+b);

end

end

meanImg(i,j)=sum/(windowSize*windowSize);

end

end

% Compute a map of variance values

for(i=1+win:1:nrwin)

for(j=1+win:1:ncwin)

sum=0.0;

for(a=win:1:win)

for(b=win:1:win)

sum=sum+((inputImage(i+a,j+b)

meanImg(i,j))^2);

end

end

var=sum/((windowSize*windowSize)1);

% Apply threshold to produce a binarized variance map

if (var > thresh)

varianceImg(i,j) = 255;

else

varianceImg(i,j) = 0;



end

end

end

% Stop the timer to calculate the time consumed.

timeTaken=toc;

Comments : 4 Comments Tags: Computer Vision, Low-texture, MATLAB code, Variance Map Categories : Computer Vision, MATLAB, Random Code, Stereo-Vision

Correlation based similarity measure-

Sum of Hamming Distances (SHD)

i1 Vote

Example 1: Tsukuba

(http://siddhantahuja.files.wordpress.com/2009/05/tsukubaleft.jpg)LeftImage

(http://siddhantahuja.files.wordpress.com/2009/05/tsukubaright.jpg)

(http://siddhantahuja.files.wordpress.com/2009/05/tsukubashd9x9disprange0-16color.png)

30 05 2009



Disparity Range: 0-16Window Size: 99

The hotter the color, the closer it is; the cooler the color the farther it is.Example 2: Stereogram

(http://siddhantahuja.files.wordpress.com/2009/05/stereogramleft.jpg)LeftImage

(http://siddhantahuja.files.wordpress.com/2009/05/stereogramshd9x9disprange0-16color.png)


The hotter the color, the closer it is; the cooler the color the farther it is.

MATLAB Code-SHD Right to Left Matching

% *************************************************************************

% Title: Function

Compute Correlation between two images using the

% similarity measure of Sum of Hamming Distances (SHD) with Right Image

% as reference.


% Created: May 2008


% Inputs: Left Image (var: leftImage), Right Image (var: rightImage),

% Window Size (var: windowSize), Minimum Disparity (dispMin), Maximum

% Disparity (dispMax)

% Outputs: Disparity Map (var: dispMap), Time taken (var: timeTaken)

% Example Usage of Function: [dispMap, timeTaken]=funcSHDR2L('TsukubaLeft.jpg', 'TsukubaRight.jpg', 9, 0, 16);

% *************************************************************************

function [dispMap, timeTaken]=funcSHDR2L(leftImage, rightImage, windowSize, dispMin, dispMax)

try

% Grab the image information (metadata) of left image using the function imfinfo

leftImageInfo=imfinfo(leftImage);

% Since SHDR2L is applied on a grayscale image, determine if the

% input left image is already in grayscale or color

if(getfield(leftImageInfo,'ColorType')=='truecolor')




% grayscale using rgb2gray function and assign it to variable leftImage

leftImage=rgb2gray(imread(leftImage));

else if(getfield(leftImageInfo,'ColorType')=='grayscale'


leftImage=imread(leftImage);

else


end

end

catch


leftImage=leftImage;

end

try

% Grab the image information (metadata) of right image using the function imfinfo

rightImageInfo=imfinfo(rightImage);

% Since SHDR2L is applied on a grayscale image, determine if the

% input right image is already in grayscale or color

if(getfield(rightImageInfo,'ColorType')=='truecolor')


% grayscale using rgb2gray function and assign it to variable rightImage

rightImage=rgb2gray(imread(rightImage));

else if(getfield(rightImageInfo,'ColorType')=='grayscale'


rightImage=imread(rightImage);

else

error('The Color Type of Right Image is not acceptable. Acceptable color types are truecolor or grayscale.'

end

end

catch


rightImage=rightImage;

end

% Find the size (columns and rows) of the left image and assign the rows to

% variable nrLeft, and columns to variable ncLeft

[nrLeft,ncLeft] = size(leftImage);

% Find the size (columns and rows) of the right image and assign the rows to

% variable nrRight, and columns to variable ncRight

[nrRight,ncRight] = size(rightImage);

% Check to see if both the left and right images have same number of rows

% and columns

if(nrLeft==nrRight && ncLeft==ncRight)

else

error('Both left and right images should have the same number of rows and columns'

end






end

% Check whether minimum disparity is less than the maximum disparity.

if (dispMin>dispMax)

error('Minimum Disparity must be less than the Maximum disparity.'

end

% Create an image of size nrLeft and ncLeft, fill it with zeros and assign

% it to variable dispMap

dispMap=zeros(nrLeft, ncLeft);




numberOfBits=8;


for(i=1+win:1:nrLeftwin)

for(j=1+win:1:ncLeftwindispMax)

min=0;

position=0;

rightWindow=rightImage(iwin:i+win, j

win:j+win);

for(dispRange=dispMin:1:dispMax)

if (j+win+dispRange dif)

min=dif;

position=dispRange;

end

end

end

dispMap(i,j) = position;

end

end


timeTaken=toc;



Comments : 7 Comments Tags: Area based, Correlation, Cost Functions, MATLAB code, SHD,Similarity measures, Stereo-matching, Sum of Hamming Distances Categories : Computer Vision, MATLAB, Random Code, Stereo-Vision


Normalized Cross Correlation (NCC)

i2 Votes

Example 1: Tsukuba



(http://siddhantahuja.files.wordpress.com/2009/05/tsukubancc9x9disprange0-16color1.png)



Example 2: Stereogram

20 05 2009




(http://siddhantahuja.files.wordpress.com/2009/05/stereogramncc9x9disprange0-16color.png)



MATLAB Code-NCC Right to Left Matching

% *************************************************************************

% Title: Function


% similarity measure of Normalized Cross Correlation (NCC) with Right Image

% as reference.


% Created: May 2008






% Example Usage of Function: [dispMap, timeTaken]=funcNCCR2L('StereogramLeft.jpg', 'StereogramRight.jpg', 9, 0, 16);

% *************************************************************************

function [dispMap, timeTaken]=funcNCCR2L(leftImage, rightImage, windowSize, dispMin, dispMax)

try



% Since NCCR2L is applied on a grayscale image, determine if the









leftImage=double(leftImage);





leftImage=double(leftImage);

else


end

end

catch



end

try



% Since NCCR2L is applied on a grayscale image, determine if the







rightImage=double(rightImage);





rightImage=double(rightImage);

else


end

end

catch



end








% and columns




else


end




end




end










prevNCC = 0.0;

bestMatchSoFar = dispMin;


ncc=0.0;

nccNumerator=0.0;

nccDenominator=0.0;

nccDenominatorRightWindow=0.0;

nccDenominatorLeftWindow=0.0;

for(a=win:1:win)

for(b=win:1:win)

nccNumerator=nccNumerator+

(rightImage(i+a,j+b)*leftImage(i+a,j+b+dispRange));

nccDenominatorRightWindow=nccDenominatorRightWindow+

(rightImage(i+a,j+b)*rightImage(i+a,j+b));

nccDenominatorLeftWindow=nccDenominatorLeftWindow+

(leftImage(i+a,j+b+dispRange)*leftImage(i+a,j+b+dispRange));

end

end

nccDenominator=sqrt(nccDenominatorRightWindow*nccDenominatorLeftWindow);

ncc=nccNumerator/nccDenominator;

if (prevNCC < ncc)

prevNCC = ncc;

bestMatchSoFar = dispRange;

end

end



dispMap(i,j) = bestMatchSoFar;

end

end


timeTaken=toc;

Comments : 9 Comments Tags: Area based, Correlation, Cost Functions, MATLAB code, NCC,Normalized Cross Correlation, Similarity measures, Stereo-matching Categories : Computer Vision, MATLAB, Random Code, Stereo-Vision


Sum of Squared Differences (SSD)

i4 Votes

Example 1: Tsukuba



20 05 2009



(http://siddhantahuja.files.wordpress.com/2009/05/tsukubassd9x9disprange0-16color1.png)


The hotter the color, the closer it is; the cooler the color the farther it is.Example 2: Stereogram


(http://siddhantahuja.files.wordpress.com/2009/05/stereogramssd9x9disprange0-16color.png)



MATLAB Code-SSD Right to Left Matching

% *************************************************************************

% Title: Function


% similarity measure of Sum of Squared Differences (SSD) with Right Image

% as reference.


% Created: May 2008






% Example Usage of Function: [dispMap, timeTaken]=funcSSDR2L('StereogramLeft.jpg', 'StereogramRight.jpg', 9, 0, 16);

% *************************************************************************

function [dispMap, timeTaken]=funcSSDR2L(leftImage, rightImage, windowSize, dispMin, dispMax)

try



% Since SSDR2L is applied on a grayscale image, determine if the









leftImage=im2double(leftImage);





leftImage=im2double(leftImage);

else


end

end

catch



end

try



% Since SSDR2L is applied on a grayscale image, determine if the







rightImage=im2double(rightImage);





rightImage=im2double(rightImage);

else


end

end

catch



end










% and columns


else


end




end




end










prevSSD = 65532;

temp=0.0;

bestMatchSoFar = dispMin;


ssd=0.0;

for(a=win:1:win)

for(b=win:1:win)

if (j+b+dispRange ssd)

prevSSD = ssd;

bestMatchSoFar = dispRange;

end

end



dispMap(i,j) = bestMatchSoFar;

end

end


timeTaken=toc;

Comments : 19 Comments Tags: Area based, Correlation, Cost Functions, MATLAB code, Similaritymeasures, SSD, Stereo-matching, Sum of Squared Differences Categories : Computer Vision, MATLAB, Random Code, Stereo-Vision

Quality metrics in Stereo-Vision

iRate This

Root-Mean-Squared (RMS) Error

This measure is computed by the following formula:

(http://siddhantahuja.files.wordpress.com/2009/05/rms-error.png)

where, N is the total number of pixels in the image, dc is the computed

disparity map, and dt is the ground truth disparity map.

MATLAB Code

% *************************************************************************

12 05 2009



% Title: FunctionCompute Root Mean Squared (RMS) Error


% Created: May 2008


% Inputs: Computed Disparity Map (var: computedDisparityMap), Ground Truth Disparity Map (var: groundTruthDisparityMap),

% How many pixels to ignore at the border (var: borderPixelsToIgnore). For

% a 9x9 windowSize used, border pixels to ignore should be (9

1)/2 or 4

% pixels, Disparity range (var: dispRange), Scale factor for groundtruth

% (var: scale)

% Outputs: RMS Error (var: rmsError), Time taken (var: timeTaken)

% Example Usage of Function: [rmsError, timeTaken]= RMSError('TsukubaSAD9x9DispRange=0

16.png','TsukubaGroundTruth.png',4,16,8);

% *************************************************************************

function [rmsError, timeTaken]= funcRMSError(computedDisparityMap,groundTruthDisparityMap,borderPixelsToIgnore,dispRange, scale)

% Read an image using imread function, and assign it to variable

% computedDisparityMap

try

computedDisparityMap=imread(computedDisparityMap);

catch


computedDisparityMap=computedDisparityMap;

end


computedDisparityMap=double(computedDisparityMap);


% groundTruthDisparityMap

groundTruthDisparityMap=imread(groundTruthDisparityMap);


groundTruthDisparityMap=double(groundTruthDisparityMap);


% variable nrComputedDisparityMap, and columns to variable ncComputedDisparityMap

[nrComputedDisparityMap,ncComputedDisparityMap] = size(computedDisparityMap);

% Find the size (columns and rows) of the image and assign the rows to

% variable nrGroundTruthDisparityMap, and columns to variable ncGroundTruthDisparityMap

[nrGroundTruthDisparityMap,ncGroundTruthDisparityMap] = size(groundTruthDisparityMap);


% and columns

if(nrComputedDisparityMap==nrGroundTruthDisparityMap && ncComputedDisparityMap==ncGroundTruthDisparityMap)

else

error('Both Computed Disparity Map and Groundtruth Disparity Map images should have the same number of rows and columns'

end

numPixels=0;

rmsError=0.0;


% Calculate rms error



for (i=borderPixelsToIgnore:1:nrComputedDisparityMap

borderPixelsToIgnore)

for(j=borderPixelsToIgnore:1:ncComputedDisparityMap

borderPixelsToIgnoredispRange)

if(groundTruthDisparityMap(i,j)~=0.0) % Ignore Pixels with unknown disparity in the groundTruthDisparityMap

rmsError= rmsError+

(abs((computedDisparityMap(i,j)*scale)

groundTruthDisparityMap(i,j))^2);

numPixels=numPixels+1;

end

end

end

rmsError=sqrt(rmsError/numPixels);


timeTaken=toc;

Percentage of Bad Matching Pixels

This measure is computed by the following formula:

(http://siddhantahuja.files.wordpress.com/2009/05/percentage-bad-matching.png)

where, N is the total number of pixels in an image, dc is the computeddisparity map, and dt is the ground truth disparity map, and _thresh isthe threshold for evaluating bad matched pixels (usually attains the value of1.0).

MATLAB Code

% *************************************************************************

% Title: Function

Compute Percentage of bad matching pixels


% Created: May 2008


% Inputs: Computed Disparity Map (var: computedDisparityMap), Ground Truth Disparity Map (var: groundTruthDisparityMap),

% How many pixels to ignore at the border (var: borderPixelsToIgnore). For

% a 9x9 windowSize used, border pixels to ignore should be (9

1)/2 or 4

% pixels, Disparity range (var: dispRange), Threshold (var: thresh), Scale



% factor for groundtruth (var: scale)

% Outputs: Percentage Bad matching pixels (var: perBADMatch), Time taken

% (var: timeTaken)

% Example Usage of Function: [perBADMatch, timeTaken]=

% funcPercentBadMatchingPixels(dispMap,'VenusGroundTruthL2R.png',4,16,1,8)

% *************************************************************************

function [perBADMatch, timeTaken]= funcPercentBadMatchingPixels(computedDisparityMap,groundTruthDisparityMap,borderPixelsToIgnore,dispRange,thresh, scale)


% computedDisparityMap

try

computedDisparityMap=imread(computedDisparityMap);

catch


computedDisparityMap=computedDisparityMap;

end


computedDisparityMap=double(computedDisparityMap);


% groundTruthDisparityMap

groundTruthDisparityMap=imread(groundTruthDisparityMap);


groundTruthDisparityMap=double(groundTruthDisparityMap);


% variable nrComputedDisparityMap, and columns to variable ncComputedDisparityMap

[nrComputedDisparityMap,ncComputedDisparityMap] = size(computedDisparityMap);


% variable nrGroundTruthDisparityMap, and columns to variable ncGroundTruthDisparityMap

[nrGroundTruthDisparityMap,ncGroundTruthDisparityMap] = size(groundTruthDisparityMap);


% and columns

if(nrComputedDisparityMap==nrGroundTruthDisparityMap && ncComputedDisparityMap==ncGroundTruthDisparityMap)

else

error('Both Computed Disparity Map and Groundtruth Disparity Map images should have the same number of rows and columns'

end

numPixels=0;

perBADMatch=0.0;


% Calculate Percentage Bad Matching Pixels

for (i=borderPixelsToIgnore:1:nrComputedDisparityMap

borderPixelsToIgnore)

for(j=borderPixelsToIgnore:1:ncComputedDisparityMap

borderPixelsToIgnoredispRange)

if(groundTruthDisparityMap(i,j)~=0.0) % Ignore Pixels with unknown disparity in the groundTruthDisparityMap

if(abs((computedDisparityMap(i,j)*scale)

groundTruthDisparityMap(i,j))>thresh)

perBADMatch=perBADMatch+1;



end

numPixels=numPixels+1;

end

end

end

perBADMatch=perBADMatch/numPixels;


timeTaken=toc;

Comments : 2 Comments Tags: MATLAB code, Percentage of bad matching pixels, Quality metric,RMS, Root mean squared error, Stereo-Vision Categories : Computer Vision, MATLAB, Random Code, Stereo-Vision

Correlation based similarity measures-

Sum of Absolute Differences (SAD)

i6 Votes

Example 1: Tsukuba



11 05 2009



(http://siddhantahuja.files.wordpress.com/2009/05/tsukubasad9x9disprange0-16color.png)



Example 2: Stereogram


(http://siddhantahuja.files.wordpress.com/2009/05/stereogramsad9x9disprange0-16color.png)



MATLAB Code-SAD Left to Right Matching-Implementation 1

123456789

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% *************************************************************************% Title: FunctionCompute Correlation between two images using the % similarity measure of Sum of Absolute Differences (SAD) with Left Image % as reference.% Author: Siddhant Ahuja% Created: May 2008% Copyright Siddhant Ahuja, 2008% Inputs: Left Image (var: leftImage), Right Image (var: rightImage),% Window Size (var: windowSize), Minimum Disparity (dispMin), Maximum% Disparity (dispMax)% Outputs: Disparity Map (var: dispMap), Time taken (var: timeTaken)% Example Usage of Function: [dispMap, timeTaken]=funcSADL2R('StereogramLeft.jpg', 'StereogramRight.jpg', 9, 0, 16);% *************************************************************************function [dispMap, timeTaken]=funcSADL2R(leftImage, rightImage, windowSize, dispMin, dispMax)try % Grab the image information (metadata) of left image using the function imfinfo leftImageInfo=imfinfo(leftImage); % Since SADL2R is applied on a grayscale image, determine if the % input left image is already in grayscale or color if(getfield(leftImageInfo,'ColorType')=='truecolor' % Read an image using imread function, convert from RGB color space to



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% Read an image using imread function, convert from RGB color space to % grayscale using rgb2gray function and assign it to variable leftImage leftImage=rgb2gray(imread(leftImage)); % Convert the image from uint8 to double leftImage=double(leftImage); else if(getfield(leftImageInfo,'ColorType')== % If the image is already in grayscale, then just read it. leftImage=imread(leftImage); % Convert the image from uint8 to double leftImage=double(leftImage); else error('The Color Type of Left Image is not acceptable. Acceptable color types are truecolor or grayscale.' end endcatch % if it is not an image but a variable leftImage=leftImage;endtry % Grab the image information (metadata) of right image using the function imfinfo rightImageInfo=imfinfo(rightImage); % Since SADL2R is applied on a grayscale image, determine if the % input right image is already in grayscale or color if(getfield(rightImageInfo,'ColorType')=='truecolor' % Read an image using imread function, convert from RGB color space to % grayscale using rgb2gray function and assign it to variable rightImage rightImage=rgb2gray(imread(rightImage)); % Convert the image from uint8 to double rightImage=double(rightImage); else if(getfield(rightImageInfo,'ColorType')== % If the image is already in grayscale, then just read it. rightImage=imread(rightImage); % Convert the image from uint8 to double rightImage=double(rightImage); else error('The Color Type of Right Image is not acceptable. Acceptable color types are truecolor or grayscale.' end endcatch % if it is not an image but a variable rightImage=rightImage;end% Find the size (columns and rows) of the left image and assign the rows to% variable nrLeft, and columns to variable ncLeft[nrLeft,ncLeft] = size(leftImage);% Find the size (columns and rows) of the right image and assign the rows to% variable nrRight, and columns to variable ncRight[nrRight,ncRight] = size(rightImage);% Check to see if both the left and right images have same number of rows% and columnsif(nrLeft==nrRight && ncLeft==ncRight)else error('Both left and right images should have the same number of rows and columns'end% Check the size of window to see if it is an odd number.



MATLAB Code-SAD Right to Left Matching-Implementation 1

75767778798081828384858687888990919293949596979899

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% 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% Check whether minimum disparity is less than the maximum disparity.if (dispMin>dispMax) error('Minimum Disparity must be less than the Maximum disparity.'end% Create an image of size nrLeft and ncLeft, fill it with zeros and assign% it to variable dispMapdispMap=zeros(nrLeft, ncLeft);% Find out how many rows and columns are to the left/right/up/down of the% central pixel based on the window sizewin=(windowSize1)/2;tic; % Initialize the timer to calculate the time consumed.for(i=1+win:1:nrLeftwin) for(j=1+win+dispMax:1:ncLeftwin) prevSAD = 65532; temp=0.0; bestMatchSoFar = dispMin; for(dispRange=dispMin:1:dispMax) sad=0.0; for(a=win:1:win) for(b=win:1:win) if (jwin+dispRange > 0) temp=leftImage(i+a,j+b)rightImage(i+a,j+b+dispRange); if(temp sad) prevSAD = sad; bestMatchSoFar = dispRange; end end dispMap(i,j) = bestMatchSoFar; endend% Stop the timer to calculate the time consumed.timeTaken=toc;

12345678

% *************************************************************************% Title: FunctionCompute Correlation between two images using the % similarity measure of Sum of Absolute Differences (SAD) with Right Image % as reference.% Author: Siddhant Ahuja% Created: May 2008% Copyright Siddhant Ahuja, 2008% Inputs: Left Image (var: leftImage), Right Image (var: rightImage),



89

1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162

% Inputs: Left Image (var: leftImage), Right Image (var: rightImage),% Window Size (var: windowSize), Minimum Disparity (dispMin), Maximum% Disparity (dispMax)% Outputs: Disparity Map (var: dispMap), Time taken (var: timeTaken)% Example Usage of Function: [dispMap, timeTaken]=funcSADR2L('StereogramLeft.jpg', 'StereogramRight.jpg', 9, 0, 16);% *************************************************************************function [dispMap, timeTaken]=funcSADR2L(leftImage, rightImage, windowSize, dispMin, dispMax)try % Grab the image information (metadata) of left image using the function imfinfo leftImageInfo=imfinfo(leftImage); % Since SADR2L is applied on a grayscale image, determine if the % input left image is already in grayscale or color if(getfield(leftImageInfo,'ColorType')=='truecolor' % Read an image using imread function, convert from RGB color space to % grayscale using rgb2gray function and assign it to variable leftImage leftImage=rgb2gray(imread(leftImage)); % Convert the image from uint8 to double leftImage=double(leftImage); else if(getfield(leftImageInfo,'ColorType')== % If the image is already in grayscale, then just read it. leftImage=imread(leftImage); % Convert the image from uint8 to double leftImage=double(leftImage); else error('The Color Type of Left Image is not acceptable. Acceptable color types are truecolor or grayscale.' end endcatch % if it is not an image but a variable leftImage=leftImage;endtry % Grab the image information (metadata) of right image using the function imfinfo rightImageInfo=imfinfo(rightImage); % Since SADR2L is applied on a grayscale image, determine if the % input right image is already in grayscale or color if(getfield(rightImageInfo,'ColorType')=='truecolor' % Read an image using imread function, convert from RGB color space to % grayscale using rgb2gray function and assign it to variable rightImage rightImage=rgb2gray(imread(rightImage)); % Convert the image from uint8 to double rightImage=double(rightImage); else if(getfield(rightImageInfo,'ColorType')== % If the image is already in grayscale, then just read it. rightImage=imread(rightImage); % Convert the image from uint8 to double rightImage=double(rightImage); else error('The Color Type of Right Image is not acceptable. Acceptable color types are truecolor or grayscale.' end endcatch % if it is not an image but a variable rightImage=rightImage;end



6263646566676869707172737475767778798081828384858687888990919293949596979899

100101102103104105106107108109110111112113114115116117

end% Find the size (columns and rows) of the left image and assign the rows to% variable nrLeft, and columns to variable ncLeft[nrLeft,ncLeft] = size(leftImage);% Find the size (columns and rows) of the right image and assign the rows to% variable nrRight, and columns to variable ncRight[nrRight,ncRight] = size(rightImage);% Check to see if both the left and right images have same number of rows% and columnsif(nrLeft==nrRight && ncLeft==ncRight)else error('Both left and right images should have the same number of rows and columns'end% 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% Check whether minimum disparity is less than the maximum disparity.if (dispMin>dispMax) error('Minimum Disparity must be less than the Maximum disparity.'end% Create an image of size nrLeft and ncLeft, fill it with zeros and assign% it to variable dispMapdispMap=zeros(nrLeft, ncLeft);% Find out how many rows and columns are to the left/right/up/down of the% central pixel based on the window sizewin=(windowSize1)/2;tic; % Initialize the timer to calculate the time consumed.for(i=1+win:1:nrLeftwin) for(j=1+win:1:ncLeftwindispMax) prevSAD = 65532; temp=0.0; bestMatchSoFar = dispMin; for(dispRange=dispMin:1:dispMax) sad=0.0; for(a=win:1:win) for(b=win:1:win) if (j+b+dispRange

After running the above code (Implementation 1-Left to Right matching)on Tsukuba images (384288 image resolution, disparity range [0,16],window size: 99), on my machine-Dell Studio XPS 435T, Intel Core i7-920CPU, 2.67GHz, 9.00GB RAM, Windows Vista OS, MATLAB R2008a, i getan average run time of about 6.587 seconds.

Comments : 23 Comments Tags: Area based, Correlation, Cost Functions, MATLAB code, SAD,Similarity measures, Stereo-matching, Sum of Absolute Differences Categories : Computer Vision, MATLAB, Random Code, Stereo-Vision

Census Transform in Image Processing

i4 Votes

Census Transform is a form of non-parametric local transform (i.e. relies onthe relative ordering of local intensity values, and not on the intensity valuesthemselves) used in image processing to map the intensity values of thepixels within a square window to a bit string, thereby capturing the imagestructure [1]. The centre pixels intensity value is replaced by the bit stringcomposed of set of boolean comparisons such that in a square window,moving left to right,

If (CurrentPixelIntensity

1. Reduces effects of variations caused by cameras gain and bias.2. Increase in robustness to outliers near depth-discontinuities.3. Encodes local spatial structure.4. Tolerant to factionalism (If a minority of pixels in a local neighborhood

has a very different intensity distribution than the majority, onlycomparisons involving a member of the minority are affected).

5. It can distinguish between rotations and reflections

Disadvantages

1. Loss of information associated with the pixel.2. As the size of the window increases, so does the requirement of the

variables size. For a census window of 33, the variable used to store

the census value would be of size 23, or 8 bits; for a census of window

size 55 the number of bits required to store census value would be 25

or 32 bits.

Example

SampleImage-Cones

(http://siddhantahuja.files.wordpress.com/2009/05/censuscones3x3.png)Transform (33)

MATLAB Code

% *************************************************************************

% Title: FunctionCensus Transform of a given Image


% Created: May 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')




else if(getfield(imageInfo,'ColorType')=='grayscale')



else

error('The Color Type of Input Image is not acceptable. Acceptable color types are truecolor or grayscale.'

end

end

catch

inputImage=inputImage;

end







end

if (windowSize==3)

bits=uint8(0);


% it to variable censusTransformedImage of type uint8

censusTransformedImage=uint8(zeros(nr,nc));

else if (windowSize==5)

bits=uint32(0);


% 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;


% central pixel

C= (windowSize1)/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


timeTaken=toc;

In order to run the above code, you should have the image processingtoolbox installed in MATLAB.

References

[1] R. Zabih and J. Woodfill, Non-parametric Local Transforms forComputing Visual Correspondence, Proceedings of the third Europeanconference on Computer Vision (Vol. II)

Comments : 4 Comments Tags: Census Filter, Census Transform, Image Processing, MATLAB code,non-parametric local transform Categories : Computer Vision, MATLAB, Random Code

Rank Transform in Image Processing

8 05 2009



i1 Vote

Rank Transform is a form of non-parametric local transform (i.e. relies onthe relative ordering of local intensity values, and not on the intensity values

themselves) used in image processing to rank the intensity values of thepixels within a square window. The centre pixels intensity value is replacedby its rank amongst the neighboring pixels [1].

Advantages

1. Reduces effects of variations caused by cameras gain and bias.2. Increase in robustness to outliers near depth-discontinuities.

Disadvantages

1. Loss of information associated with the pixel.2. Rank transform cannot distinguish between rotations and reflections,

and has been shown to produce the same rank for variety of patterns[2].

Example

Sample Image-Cones



(http://siddhantahuja.files.wordpress.com/2009/05/rankcones7x7.png)RankTransform (77)MATLAB Code

% *************************************************************************

% Title: FunctionRank Transform of a given Image


% Created: May 2008


% Inputs: Image (var: inputImage), Window size assuming square window (var:

% windowSize)

% Outputs: Rank Tranformed Image (var: rankTransformedImage),

% Time taken (var: timeTaken)

% Example Usage of Function: [a,b]=funcRankOneImage('Img.png', 3)

% *************************************************************************

function [rankTransformedImage, timeTaken] = funcRankOneImage(inputImage, windowSize)

% Grab the image information (metadata) using the function imfinfo

imageInfo=imfinfo(inputImage);

% Since Rank Transform is applied on a grayscale image, determine if the

% input image is already in grayscale or color

if(getfield(imageInfo,'ColorType')=='truecolor')




else if(getfield(imageInfo,'ColorType')=='grayscale')



else

error('The Color Type of Input Image is not acceptable. Acceptable color types are truecolor or grayscale.'

end

end




end

% Initialize the timer to calculate the time consumed.

tic;





% it to variable rankTransformedImage

rankTransformedImage = zeros(nr,nc);



R= (windowSize1)/2;



for (i=R+1:1:nr

R) % Go through all the rows in an image (minus R at the borders)

for (j=R+1:1:nc

R) % Go through all the columns in an image (minus R at the borders)

rank = 0; % Initialize default rank to 0

for (a=

R:1:R) % Within the square window, go through all the rows

for (b=

R:1:R) % Within the square window, go through all the columns

% If the intensity of the neighboring pixel is less than

% that of the central pixel, then increase the rank

if (inputImage(i+a,j+b) < inputImage(i,j))

rank=rank+1;

end

end

end

% Assign the rank value to the pixel in imgTemp

rankTransformedImage(i,j) = rank;

end

end


timeTaken=toc;

In order to run the above code, you should have the image processingtoolbox installed in MATLAB.

Experiment with Varying Patterns

(http://siddhantahuja.files.wordpress.com/2009/05/testpattern1.png)Test Pattern 1

(http://siddhantahuja.files.wordpress.com/2009/05/testpattern2.png)



(http://siddhantahuja.files.wordpress.com/2009/05/testpattern4.png)Test Pattern 4

(http://siddhantahuja.files.wordpress.com/2009/05/testpattern5.png)

All of the above patterns yield the following rank result:

(http://siddhantahuja.files.wordpress.com/2009/05/testpatternrankresult.png)

with a rank of 4.

Thus proving that Rank transform cannot distinguish betweenrotations and reflections, and has been shown to produce the same rank forvariety of patterns [2]. More of these patterns can be generated toemphasize the point.

References

[1] R. Zabih and J. Woodfill, Non-parametric Local Transforms forComputing Visual Correspondence, ECCV 2004, pp. 151-158

[2] J. Banks, P. Corke, Quantitative Evaluation of Matching Methods andValidity Measures for Stereo Vision, IJRR, 2001; 20; 512

R. Zabih and J. Woodfill, Non-parametric LocalTransforms for Computing Visual Correspondence, ECCV2004, pp. 151-158

Comments : 1 Comment Tags: Image Processing, MATLAB code, non-parametric local transform,Rank Filter, Rank Transform Categories : Computer Vision, MATLAB, Random Code

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