Color differentation in the image

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hamed abdulaziz 2014-2-15

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https://ww2.mathworks.cn/matlabcentral/answers/116279-color-differentation-in-the-image

移动：DGM 2023-12-30

采纳的回答： Image Analyst

Hi All,

Is there any code to find differentation in the image?

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采纳的回答

Image Analyst 2014-2-15

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https://ww2.mathworks.cn/matlabcentral/answers/116279-color-differentation-in-the-image#answer_124498

What do you mean by differentiation? I have several color segmentation demos in my File Exchange if that's what you mean: http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862

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hamed abdulaziz 2014-2-17

编辑：hamed abdulaziz 2014-2-17

how can determine the the number of(rows and columns) in the

LStandard = LMean *

ones(rows, columns);

and can I use the value of deltaE in the

deltaE = sqrt(deltaL

.^ 2 + deltaa .^ 2 +

deltab .^ 2);

as a feature for color variation?

I tried this code

clear all;

clc;

close all;

% rgbImage= imread('D:\final_segmentation\segmentation_abnormal\200AC.BMP');

rgbImage= imread('D:\3.BMP');

% Find the mask something like this:

% Extract the individual red, green, and blue color channels.

redChannel = rgbImage(:, :, 1);

greenChannel = rgbImage(:, :, 2);

blueChannel = rgbImage(:, :, 3);

% Find where all of them are zero.

mask = redChannel ==

0 & greenChannel ==

0 & blueChannel ==

0; % Then convert rgb to lab and get mean lab like this:

% Convert image from RGB colorspace to lab color space.

cform =

makecform('srgb2lab'

);

lab_Image =

applycform(im2double

(rgbImage),cform); % Extract out the color bands from the original image % into 3 separate 2D arrays, one for each color component.

LChannel = lab_Image(:, :, 1);

aChannel = lab_Image(:, :, 2);

bChannel = lab_Image(:, :, 3);

LMean =

mean(LChannel(mask));

aMean =

mean(aChannel(mask));

bMean =

mean(bChannel(mask));

[rows,

columns]=size(LChann

el);

% Make uniform images of only that one single LAB color.

LStandard = LMean *

ones(rows, columns);

aStandard = aMean *

ones(rows, columns);

bStandard = bMean *

ones(rows, columns); % Create the delta images: delta L, delta A, and delta B.

deltaL = LChannel - LStandard;

deltaa = aChannel - aStandard;

deltab = bChannel - bStandard; % Create the Delta E image. % This is an image that represents the color difference. % Delta E is the square root of the sum of the squares of the delta images.

deltaE = sqrt(deltaL .^ 2 + deltaa .^ 2 + deltab .^ 2);

but I always get deltaE = zero

thank you

Image Analyst 2014-2-17

移动：DGM 2023-12-30

在 MATLAB Online 中打开

Looks about right for as far as you got. You just need to complete it to find the variation using code I gave you in my answer:

pixelsInsideMask = deltaE(mask); % 1D vector of pixels only within mask.
counts = hist(pixelsInsideMask, numberOfBins);
meanDeltaE = mean(pixelsInsideMask(:));
SDDeltaE = std(pixelsInsideMask(:));