The document discusses image segmentation techniques. It begins by defining segmentation as partitioning an image into distinct regions that correlate with objects or features of interest. The goal of segmentation is to find meaningful groups of pixels. Several segmentation techniques are described, including region growing/shrinking, clustering methods, and boundary detection. Region growing uses homogeneity tests to merge neighboring regions, while clustering divides space based on similarity within groups. Boundary detection finds boundaries between objects. The document provides examples and details of applying these segmentation methods.

1. Segmentation
Objective:
Student will learn on how to find
regions that represent objects or
meaningful part of objects.
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2.  Download di http://rumah-belajar.org
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3. Introduction
 Segmentation is generally the first stage in any
attempt to analyze or interpret an image
automatically.
 Image segmentation is important in many computer
vision and image processing applications.
 Segmentation partitions an image into distinct
regions that are meant to correlate strongly with
objects or features of interest in the image.
 Segmentation can also be regarded as a process of
grouping together pixels that have similar attributes.
 For segmentation to be useful, the regions or groups
of pixels that we generate should be meaningful.
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4.  Segmentation bridges the gap between
low-level image processing, which
concerns itself with manipulation of pixel
grey level or color to correct defects or
enhance certain characteristics of the
image, and high-level processing, which
involves the manipulation and analysis of
groups of pixel that represent particular
features of interest.
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5.  Some kind of segmentation technique
will be found in any application
involving the detection, recognition and
measurement of objects in image.
 Examples
 Industrial inspection
 Optical character recognition (OCR)
 Tracking of objects in a sequence of
images
 Classification of terrains visible in satellite
images
 Detection and measurement of bone,
tissue, etc., in medical images.
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6.  The goal of image segmentation is to
find regions that represent objects or
meaningful parts of objects.
 Division of the image into regions
corresponding to objects of interest is
necessary before any processing can be
done at a level higher that that of the
pixel.
 Identifying real objects, pseudo objects
and shadows or actually finding
anything of interest within the image
requires some form of segmentation.
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7.  The role of segmentation is crucial in most tasks requiring
image analysis.
 The success or failure of the task is often a direct
consequence of the success or failure of segmentation.
 Segmentation techniques can be classified as either
contextual or non-contextual.
 Non-contextual technique ignore the relationships that
exist between features in an image.
 Pixels are simply grouped together on the basis of some global
attribute, such as grey level.
 Contextual technique exploit the relationships between
grey image features.
 Group together pixels that have similar grey levels and are close to
one another.
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8. Overview
 Image segmentation methods will look for
objects that either have some measure of
homogeneity within themselves or have some
measure of contrast with the objects on their
border.
 Most image segmentation algorithm are
modifications, extensions or combinations of
these two basic concepts.
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9.  The homogeneity and contrast
measures can include features such as
grey level, color and texture.
 After performed some preliminary
segmentation, we may incorporate
higher-level object properties, such as
perimeter and shape, into the
segmentation process.
 The major problems are a result of
noise in the image and digitization of a
continuous image.
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10.  Noise is typically caused by the camera,
the lenses, the lighting, or the signal
path and can be reduced by the use of
the pre-processing methods.
 Spatial digitization can cause problems
regarding connectivity of objects.
 These problems can be resolved with
careful connectivity definitions and
heuristics applicable to the specific
domain.
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11. Connectivity
 Connectivity refers to the way in which we
define an object.
 After we have segmented an image, which
segments should be connected to form an
object?
 Or at lower level, when searching the image
for homogeneous regions, how do we define
which pixels are connected?
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12.  We can define connectivity in three
different ways:
1. 4-connectivity
2. 8-connectivity, and
3. 6-connectivity
Which is which?
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13. 6-connectivity NW/SE 6-connectivity NE/SW
•Which definition is chosen depends on the application,
but the key to avoiding problems is to be consistent.
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14.  We can divide image segmentation
techniques into 3 main categories:
1. Region growing and shrinking
2. Clustering methods, and
3. Boundary detection.
 The region growing and shrinking methods
use the row and column or x and y based
image space.
 Clustering techniques can be applied to any
domain (spatial domain, color, space,
feature space, etc.)
 The boundary detection methods are
extensions of the edge detection
techniques.
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15. Region Growing and Shrinking
 Segment the image into regions by
operating principally in rc/xy-based
image space.
 Some are local, others are global, and
combine split and merge.
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16.  Split and merge technique 
1. Define a homogeneity test. A measurement
which incorporate brightness, color, texture,
or other application-specific information, and
determining a criterion the region must meet
to pass the homogeneity test.
2. Split the image into equally sized regions.
3. It the homogeneity test is passed for a
region, then merge is attempted with its
neighbour (s). If the criterion is not met, the
region is split.
4. Continue this process until all regions pass
the homogeneity test.
 There are many variations of this algorithm.
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17.  The user defined homogeneity test is
largely application dependent.
 The general idea is to look for features
that will be similar within an object and
different from the surrounding objects.
 In the simplest case  use grey level
as feature of interest.
 Could use the grey level variance as
homogeneity measure and define a
homogeneity test that required the grey
level variance within a region to be less
than some threshold.
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18.  We can define grey-level variance as 
1 2
f ( x, y ) I
N 1 ( x, y ) region
1
where I f ( x, y )
N ( x, y ) region
•The variance is basically a measure of how
widely the grey level within a region vary.
•Higher order statistic can be used for features
such as texture.
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19. Clustering Technique
 Clustering techniques are image segmentation
methods which individual elements are placed into
groups based on some measure of similarity within
the groups.
 The simplest method is to divide the space of interest
into regions by selecting the centre or median along
each dimension and splitting it.
 Can be done iteratively until the space is divided into
specific number of regions needed.  used in the
SCT/Center and PCT/Median segmentation
algorithms.
  will be effective only if the space and the entire
algorithm is designed intelligently.
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20.  Recursive region splitting is a clustering
method that has become a standard
technique.
 One of the 1st algorithms based on recursive
region splitting
1. Consider the entire image as one region and
computer histograms for each component of
interest (red, green and blue for a color image).
2. Apply a peak finding test to each histogram.
Select the best peak and put thresholds on
either side of the peak. Segment the image into
two regions based on this peak.
3. Smooth the binary threshold image so that only
a single connected sub-region is left.
4. Repeat step 1-3 for each region until no new
sub-regions can be created  no histograms
have significant peaks.
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21. 2 threshold are selected, one on each side of the best
peak. The image is then split into two regions. Region 1
corresponds to those pixels with feature values between
the selected thresholds. Region 2 consists of those pixels
with feature values outside the threshold. 21

22. Many of the parameters of this algorithm are application
specific. What peak-finding test do we use? And what is
a significant peak? 22

23.  Other Clustering Technique
1. SCT/Center segmentation, and
2. PCT/Median segmentation.
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25. Boundary Detection
 Performed by finding
the boundaries between
object defining the
objects.
 Other segmentation
technique include
Combined approaches
and Morphological
Filtering.
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