Automatic Image Segmentation by Dynamic Region Growth And
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Transcript of Automatic Image Segmentation by Dynamic Region Growth And
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Defined as classification of all the picture
elements or pixels in an image into different
clusters that exhibit similar features. Color , edges and textures are used as
properties.
Applications include object classification ,
image retrieval and medical imaging analysis,
brain tumor detection and fingerprinting.
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A new unsupervised color image
segmentation algorithm is proposed known as
G- SEGmentation algorithm. This algorithm exploits the information
obtained from detecting edges in color
images in CIE L*a*b* color space.
Pixels without edges are clustered and
labeled individually using color gradient
detection technique.
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Texture modeling is performed by color
quantization and local entropy of quantized
image. The obtained color and texture along with a
region growth map consisting of all fully
grown regions are used to perform a unique
multi-resolution merging procedure to
blend regions with similar characteristics.
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The detected areas with no edges inside them
are the initial clusters or seeds selected to
initiate the segmentation of the image. The pixels that compose each detected region
receive a label and the combination of pixels
with the same label is referred as a seed.
These seeds grow into the higher edge
density areas, and additional seeds are created
to generate an initial segmentation map.
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1. Selects clusters for images using gradient
information in CIE L*a*b* color space.
2. Characterizes the texture present in eachcluster.
3. Generates a final segmentation map by
utilizing an effective merging approach.
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Consists of three different modules as shown
in fig.1.
First module implements edge-detectionalgorithm to produce an edge map used in
generation of adaptive gradient thresholds,
which in turn dynamically select regions of
contiguous pixels that display similar
gradient and color values, producing an initial
segmentation map.
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The second module creates a texture
characterization channel by first quantizing
the input image, followed by entropy based
filtering of the quantized colors of the image.
The last module utilizes the initial
segmentation map and the texture channel to
obtain our final segmentation map.
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Here regions where the gradient map displays
no edges are searched.
The selected regions form the initial set ofseeds to segment the image.
The region growth procedure also accounts
for regions, which display similar edge values
throughout, by detecting unattached regions
at various edge density levels.
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Patterns are composed of multiple shades of colors
causing over-segmentation and misinterpretation of
the edges surrounding these regions.
Texture regions may contain regular patterns such as
a brickwall, or irregular patterns such as leopard
skins, bushes, and many objects found in nature.
A method for obtaining information of patternswithin an image is to evaluate the randomness
present in various areas of that image.
Entropy provides a measure of uncertainty of a
random variable
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Here an effective method is incorporated to analyze
grouped data from the statistical field, to merge all over
segmented regions.
This method better known as a multivariate analysisallows to take regions that have been separated due to
occlusion, or small texture differences, and merge them
together.
The core of a multivariate analysis lies in highlighting thedifferences between groups that display multiple variables to
investigate the possibility that multiple groups are associated
with a single factor.
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Using a multivariate analysis approach of all
independent regions, the resultant distances
between groups is used to merge similar
regions.
Since the image has been segmented into
different groups, information can be gathered
from each individual region.
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To prevent the need to re-evaluate the
distances for various groups after each stage
of the region merging procedure, an alternate
approach is introduced.
Having the distances between groups, the
smallest distance value is found,
corresponding to a single pair of groups.
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The similarity value is increased until a larger
set of group pairs is obtained.
The smallest group is merged first in this setand then continues to merge the next larger
group.
After the first merge, a check is performed to
see if one of the groups being merged is now
part of a larger group.
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In this case all the pair combinations of the
groups should belong to the pairs selected
initially in the set to be merged together.
Once all the pairs of the set have been
processed, the distance is recomputed for the
new segmentation map, and the process is
repeated.
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In problems such as segmentation, multi-resolution analysis offers two key advantagesover pixel-based methods:
1. It provides a way to trade-off class and spatialresolution; repeatedly blurring and sub-sampling the image decreases the noise andimproves the class certainty, but at the expenseof spatial resolution .
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2. The use of a multi-resolution technique ensures
both robustness in noise and efficiency
of computation.
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Result of G-SEG algorithm is as shown in
figure below.
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The input RGB image and its CIE L*a*b*
counterpart are shown in Fig. 2(a) and (b),
respectively.
The outcome of gradient computation on the colorconverted input image, shown in Fig. (2c).
The seed map at the end of the region growth
procedure, obtained utilizing thresholds that are
generated adaptively, is displayed in Fig. 2(d).
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The texture channel generated using color
quantization and local entropy calculation is
depicted in Fig. 2(e)
The segmentation map at the end of the
region merging algorithm is shown in Fig.
2(f).
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The Face image in Fig. 3(a) represents a
moderately complete image with dissimilar
texture content associated with the skin, hat
and robe of the person.
Observe that in Fig. 3(b) and 3(c), the GRF
and JSEG algorithms over segment this
image due to the texture and illuminationdisparity seen in various regions.
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The texture model has been effective in
handling different textures as seen in Fig.
3(d).
The algorithm employs the CIE L*a*b* color
space where the L* channel contains the
luminance information in the image,
incapacitates the illumination problem.
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The GSEG algorithm is primarily based on
color-edge detection, dynamic region growth,
and culminates in a unique multi-resolution
region merging procedure. The algorithm is
robust to various image scenarios and is
superior to the results obtained on the same
image when segmented by other methods.
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