| Image
Processing Articles
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| Brief
Discription: |
| Connected
components labeling
scans an image and
groups its pixels
into components based
on pixel connectivity,
i.e. all pixels in
a connected component
share similar pixel
intensity values and
are in some way connected
with each other. Once
all groups have been
determined, each pixel
is labeled with a
graylevel or a color
(color labeling) according
to the component it
was assigned to. |
|
| Extracting
and labeling of various
disjoint and connected
components in an image
is central to many
automated image analysis
applications. |
|
| How
It Works |
| Connected
component labeling
works by scanning
an image, pixel-by-pixel
(from top to bottom
and left to right)
in order to identify
connected pixel regions,
i.e. regions of adjacent
pixels which share
the same set of intensity
values V. (For a binary
image V={1}; however,
in a graylevel image
V will take on a range
of values, for example:
V={51, 52, 53, ...,
77, 78, 79, 80}.) |
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| Connected
component labeling
works on binary or
graylevel images and
different measures
of connectivity are
possible. However,
for the following
we assume binary input
images and 8-connectivity.
The connected components
labeling operator
scans the image by
moving along a row
until it comes to
a point p (where p
denotes the pixel
to be labeled at any
stage in the scanning
process) for which
V={1}. When this is
true, it examines
the four neighbors
of p which have already
been encountered in
the scan (i.e. the
neighbors (i) to the
left of p, (ii) above
it, and (iii and iv)
the two upper diagonal
terms). Based on this
information, the labeling
of p occurs as follows: |
|
 If
all four neighbors
are 0, assign a new
label to p, else
if
only one neighbor
has V={1}, assign
its label to p, else
if
one or more of the
neighbors have V={1},
assign one of the
labels to p and make
a note of the equivalences. |
|
| After
completing the scan,
the equivalent label
pairs are sorted into
equivalence classes
and a unique label
is assigned to each
class. As a final
step, a second scan
is made through the
image, during which
each label is replaced
by the label assigned
to its equivalence
classes. For display,
the labels might be
different graylevels
or colors. |
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| Guidelines
for Use |
| To
illustrate connected
components labeling,
we start with a simple
image containing some
distinct artificial
objects( specifically
text) |
|
 |
|
| Now
we apply Grayscale
conversion to the
image to convert it
to Grayscale image. |
|
 |
|
| Now,
we will convert the
image into binary
so only two intensities
will be present in
the image..... Black
and white or in other
words 0 and 1. |
|
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|
| After
scanning this image
and labeling the distinct
pixels classes with
a different grayvalue,
we obtain the labeled
output image. |
|
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| C#
Sample Program: |
The
algorithm is coded
in C# using unsafe
so the quality and
speed of the program
may not be affected.
The class BitmapData
is used to read and
process the pixels
in the image. This
is the speicality
of C# to provide such
a speed even on image
processing applications.
There is a set of
modules that are designed
to implement the algorithm.
The program scans
the image to convert
the image into grayscale
Levels.Then it converts
the image into binary.This
binary image will
be subjected to spliting
it into components.Here
we can us 4-Connected
or 8-Connected Algorithm
but we used 8-Connected
Algorithm for finding
the Blobs in the image
as described above.
The final output is
a colored image showing
separate colors for
every blob.... An
array named objects
of Type Class Objects
contains the sizes
of each blob or object
being segmented.... |
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