Activity 15: Color Image Processing

For this activity, we are to perform 2 different white balancing techniques, called the reference white algorithm and the gray world algorithm. In the reference white algorithm, a reference white object's pixel values are to be used to normalize the red, green and blue channels of the whole image. On the other hand, gray world algorithm uses the mean of each of the RGB channel values as the normalization factor.
We used two sets of objects for this activity. One set is a set of objects with different colors and another set is a set of objects with the same colors. For my second set I used a blue set of objects. The images were taken under a fluorescent lamp using the "tungsten", "daylight" and "cloudy" white balancing settings of a Canon Powershot A460 digital camera.

The following are the results for object with different colors:

TUNGSTEN

CLOUDY

DAYLIGHT

For all the images, Reference white algorithm seems to work better since the gray world algorithm requires a certain constant to be multiplied to reduce the brightness of the resulting image. For this part the constant used to limit the brightness is 0.75.

The following are the results for different blue objects under different white balancing conditions.

CLOUDY

DAYLIGHT


TUNGSTEN

For images with the same color, the reference white algorithm clearly performs better. The results of the gray world algorithm produced images with slightly redder image. A possible reason is because an average of an ensemble of colors may become white but an ensemble of objects having similar colors is not necessarily white. Hence, the white reference algorithm is expected to perform better.

Scilab Code:
I = imread("IMG_0921_cloudy.jpg");

//start of reference white algorithm
imshow(I);
pos = locate(1);
Rw = I(pos(1),pos(2),1);
Gw = I(pos(1),pos(2),2);
Bw = I(pos(1),pos(2),3);

//Start of gray world algorithm
//Rw = mean(I(:,:,1));
//Gw = mean(I(:,:,2));
//Bw = mean(I(:,:,3));

I(:,:,1) = I(:,:,1)/Rw;
I(:,:,2) = I(:,:,2)/Gw;
I(:,:,3) = I(:,:,3)/Bw;
//I = I*0.5;
I(I>1) = 1;

imwrite(I,"enhanced_cloudy.jpg");
----------------------------------------------

10/10 for this activity since I was able to finish the activity quickly and correctly.
Collaborator: Eduardo David

Activity 14: Stereometry

Activity 13: Photometric Stereo

In this activity we are to reconstruct an image using different images taken from the same point with different point source locations. The following are the images used with their corresponding point source positions



Using the following formula, we were able to obtain a 3d plot for the original image

loadmatfile("Photos.mat");
scf(0);imshow(I1,[]);
scf(1);imshow(I2,[]);
scf(2);imshow(I3,[]);
scf(3);imshow(I4,[]);
V1 = [0.085832, 0.17365, 0.98106];
V2 = [0.085832, -0.17365, 0.98106];
V3 = [0.17365, 0, 0.98481];
V4 = [0.16318, -0.34202, 0.92542];

I(1,:) = I1(:)';
I(2,:) = I2(:)';
I(3,:) = I3(:)';
I(4,:) = I4(:)';
//I = cat(1,I1(:)',I2(:)',I3(:)',I4(:)');
V = cat(1,V1,V2,V3,V4);

g = inv(V'*V)*V'*I;
N = size(g);

for i = 1:N(2)
gMag(i) = sqrt(g(1,i)**2+g(2,i)**2+g(3,i)**2+0.0000000000001);
end

nh(1,:) = (g(1,:)./gMag');
nh(2,:) = (g(2,:)./gMag');
nh(3,:) = (g(3,:)./gMag')+0.0000000000001;

fx = -nh(1,:)./nh(3,:);
fx = matrix(fx,[128,128])
fy = -nh(2,:)./nh(3,:);
fy = matrix(fy,[128,128]);
f = cumsum(fx,2)+cumsum(fy,1);

plot3d(1:128,1:128,f);


I will grade myself 10 for this activity since I was able to meet the objectives.
Collaborator: Eduardo David

Activity 12