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Connected
Component Labeling (CCL) is one of the important stages for many image analysis
systems.
CCL is the
component separation process for binary images (there is only two colors, 1 for
black and 0 for white or wise verse). This method scans the image and groups adjacent
pixels according to connection property. If every group expresses a shape,
there are shapes as many as groups. After grouping adjacent pixels, groups are
painted varied colors expressing the difference.
Samples for CCL
 
(a) (b)
Figure 1. (a) original binary image, (b) connected
components painted in different color
(a) (b)
Figure
2. (a) original binary image, (b) connected components
(a) (b)
Figure
3. (a) original binary image, (b) connected components
C Source Code
Below
there is only connected component labeling procedure. The complete program can
be found
here. Note that this
procedure is little buggy. If you want to use it please be careful.
int Connected_Com_Label(long **img_data, long width, long height)
{
long i, j, k, z, max, used_num[256]={0};
long mx, pix[9];
// prepare to binary image format
for(i=0;i<height;i++)
{
for(j=0;j<width;j++)
{
if (img_data[i][j] == 255)
img_data[i][j] = 0;
else
img_data[i][j] = 1;
}
}
z = 0;
max = 2;
for(i=1;i<height-1;i++)
{
for(j=1;j<width-1;j++)
{
pix[0] = img_data[i][j];
pix[1] = img_data[i-1][j];
pix[2] = img_data[i-1][j+1];
pix[3] = img_data[i][j+1];
pix[4] = img_data[i+1][j+1];
pix[5] = img_data[i+1][j];
pix[6] = img_data[i+1][j-1];
pix[7] = img_data[i][j-1];
pix[8] = img_data[i-1][j-1];
if (pix[0] == 1)
{
// find maximum of pixeles.
mx = 0;
for(k=1;k<9;k++)
{
if (pix[k] > mx)
mx = pix[k];
}
if (mx == 1) // if neighbours are equal to ONE, set
// it to max and increase max by ONE
{
img_data[i][j] = max;
max++;
}
else
{
pix[0] = 0;
QuickSort(pix,0, 8);
k=8;
while((k>0) && (pix[k] != 1))
k--;
if (k < 8)
img_data[i][j] = pix[k+1];
else
img_data[i][j] = pix[k];
}
}
}
}
k = 0;
for(i=0;i<height;i++)
{
for(j=0;j<width;j++)
{
mx = img_data[i][j];
if (mx > 0)
{
if (not_in_array(mx, used_num, k))
{
used_num[k] = mx;
k++;
}
}
}
}
for(i=0;i<height;i++)
{
for(j=0;j<width;j++)
{
if (img_data[i][j] == 10)
img_data[i][j] = 0;
else if (img_data[i][j] == 9)
img_data[i][j] = 25;
else if (img_data[i][j] == 8)
img_data[i][j] = 50;
else if (img_data[i][j] == 7)
img_data[i][j] = 75;
else if (img_data[i][j] == 6)
img_data[i][j] = 100;
else if (img_data[i][j] == 5)
img_data[i][j] = 125;
else if (img_data[i][j] == 4)
img_data[i][j] = 100;
else if (img_data[i][j] == 3)
img_data[i][j] = 150;
else if (img_data[i][j] == 2)
img_data[i][j] = 200;
else if (img_data[i][j] == 1)
img_data[i][j] = 225;
else
img_data[i][j] = 255;
}
}
return 0;
}
References
[1] Robert
Fisher, Simon Perkins, Ashley Walker, Erik Wolfart, “HYPERMEDIA IMAGE
PROCESSING REFERENCE”,
http://www.dai.ed.ac.uk/HIPR2/,
2002.
[2] Jung-Me Park,
Carl G. Looney, Hui-Chuan Chen, “Fast Connected Component Labeling Algorithm Using
A Divide and Conquer Technique”.
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