# Image Segmentation

Τεχνίτη Νοημοσύνη και Ρομποτική

6 Νοε 2013 (πριν από 4 χρόνια και 7 μήνες)

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Chapter
10

Image segmentation subdivides an image into its constituent
regions or objects.

The level of detail to which the subdivision is carried depends on
the problem being solved.

That is, segmentation should stop when the objects or regions of
interest in an application have been detected.

Segmentation accuracy determines the eventual success or
failure of computerized analysis procedures.

For this reason mentioned in the above point a spatial care
should be taken to improve the probability of accurate
segmentation.

Most of the segmentation algorithms used in this chapter are based on
one of two basic properties of intensity values: discontinuity and
similarity.

In the first category, the approach is to partition the image based on
abrupt changes in intensity, such as edges.

The principal approaches in the second category are based on
partitioning an image into regions that are similar according to a set of
predefined criteria.

Thresholding, region growing, and region splitting and merging are
examples of methods in this category.

In this chapter, we discuss and illustrate a number of these approaches
and show that improvements in segmentation performance can be
achieved by combining methods from distinct categories, such as
techniques in which edge detection is combined with thresholding.

Suppose the Image R using the image segmentation process, R will be partitioned into n
sub regions: R
1

, R
2

,…,
R
n

.

In the segmentation process there are five possible conditions can be obtained:

(a)
n
U
i
=
1

R
i

= R.

(b)
R
i

is
is

a connected set,
i
=
1
,
2
, … , n.

(c)
R
i

R
i

= Ø for all
i

and j,
i

≠ j.

(d) Q(
R
i

) = TRUE for
i

=
1
,
2
, . . .,n.

(e) Q(
R
i

R
i

) = FALSE for any adjacent regions
R
i

and
R
i

.

here, Q(
R
k

) is a logical predicate defined over the point in set
R
k
,

and Ø is the null set.
The symbols ∪ and ∩ represent set union and intersection, respectively. Two regions
R
i

and
R
i

are said to be adjacent if their union forms a connected set.

Condition (a) indicates that the segmentation must be
complete; that is, every pixel must be in a region.

Condition (b) requires that points in a region be connected in
some predefined sense(e.g., the points must be
4
-

or
8
-

connected.

Condition (c) indicates that the regions must be disjoint.

Condition (d) deals with the properties that must be satisfied
by the pixels in a segmented region for example, Q(
R
i
) =
TRUE if all pixels in
R
i

have the same intensity level.

Condition (e) indicates that two adjacent regions
R
i

and
R
j

must be different in the sense of predicate Q.

Thus we see that the fundamental problem in segmentation is to
partition an image into regions that satisfy the preceding conditions.

Segmentation algorithms for monochrome images generally are based
on one of the two basic categories dealing with properties of intensity
values: discontinuity and similarity.

In the first category, the assumption is that boundaries of the regions
are sufficiently different from each other and form the background to
allow boundary detection based on local discontinuities in the intensity.

Edge
-
based segmentation
is the principle approach used in this
category.

Region
-
based segmentation
approaches in the second category are
based on partitioning an image into regions that are similar according to
a set of predefines criteria.

In (a) shows an image of a region of constant intensity
superimposed on a darker background, also of constant
intensity on the foreground. These two regions comprise the
overall image region.

In (B) shows the result of computing the boundary of the
inner region based on intensity discontinuities. Points on the
inside and outside of the boundary are black (zero) because
there are no discontinuities in intensity in those regions. To
segment the image, we assign one level (say, white) to the
pixels on, or interior to the boundary and another level (say,
black) to all points exterior to the boundary.

In (c) shows the result of such a procedure.

In (d) is: if a pixel is on, or inside the boundary, label it white;
otherwise label it black.

We see that this predicate is TRUE for the points labeled black
and white in Fig.
10.1
(c).

Similarly the two segmented regions(object and background) satisfy
condition (e).

The next three images illustrate region
-
based segmentation.

In (d) in similar to (a) but the intensities of the inner region form a
textured pattern

In (e) it shows the result of computing the edges of this image
clearly it is difficult to identify a unique boundary because many of
non
-
zero intensity changes are connected to the boundary, so
edge
-
based segmentation is not a suitable approach.

To solve this problem a predicate that differentiate between
textured and constant regions. The standard deviation is used for
this purpose because it is non
-
zero (i.e. if the predicate was TRUE)
in textured region and zero otherwise.

Finally note that these results also satisfy the five conditions stated
at the beginning of this section.

The focus in this section is on segmentation methods that are
based on detecting sharp, local changes in intensity.

The three types of image features in which we are interested
are isolated points, lines, and edges.

Edge pixels are pixels at which the intensity of an image
function changes abruptly (suddenly), and edges or edge
segments are sets of connected edge pixels. Edge detectors
are local image processing methods designed to detect edge
pixels.

A line may be viewed as an edge segment in which the
intensity of the background on either side of the line is either
much higher or much lower than the intensity of the line
pixels.

As we know that local averaging smoothes an image. Given that
averaging is analogous to integration, so abrupt and local changes
can be detected using derivatives. First
-

and
-

second derivatives are
best suited for this purpose.

The following approximations should be used for the first derivative:

Must be zero in areas of constant intensity

Must be nonzero at the onset of an intensity step or ramp.

Must be nonzero at points along an intensity ramp.

The following approximations should be used for the second
derivative:

Must be zero in areas of constant intensity

Must be nonzero at the onset and end of an intensity step or ramp

Must be zero along intensity ramps.

To illustrate this and to highlight the fundamental similarities and
differences between first and second derivatives in the context of
image processing, consider
Fig.
10.2
.

In (a) it shows an image that contains various solid objects, a line,
and a single noise point.

In(b) it shows a horizontal intensity profile (scan line) of the image
approximately through its center, including the isolated point.

Transitions in intensity between solid objects and the background
along the scan line show two types of edges: ramp edges (on the
left) and step edges (on the right), intensity transitions involving thin
objects such as lines often are referred to as roof edges.

In (c) it shows a simplification of the profile, with just enough points
to make it possible for us to analyze numerically how the first
-
and
-
second order derivatives behave as they encounter a noise point, a
line, and the edges objects.

1.
First
-
order derivatives generally produce thicker edges in an
image.

2.
Second
-
order derivatives have a stronger response to fine details,
such as thin lines, isolated points, and noise.

3.
Second
-
order derivatives produce a double
-
edge response at
ramp and step transitions in intensity.

4.
The sign of the second derivative can be used to determine
whether a transition into an edge is from light to dark or dark to
light.

5.
The approach of choice for computing first and second derivatives
at every pixel location in an image is to use spatial filters.

Based on the previous conclusions, we know that point
detection should be based on the second derivative. This
implies using the Laplacian filter.

The next level of complexity is line detection.

Based on previous conclusions, we know that for line
detection we can expect second derivatives to result in a
stronger response and to produce thinner lines than first
derivatives.

Thus the Laplacian mask. Also keep in mind that double
-
line
effect of the second derivative must be handled properly.

The following example illustrates the procedure in Fig.
10.5

In (a) it shows a
486
x
486
binary portion of an image.

In (b) it shows its Laplacian Image. Since Laplacian contains negative
values, scaling is necessary for display. As the magnified section
shows, mid gray represents zero, darker gray shades represents
negative values, and lighter shades are positive. The double line
effect is clearly visible in the magnified region.

First, it might appear that the negative values can be handled simply
by taking the absolute value of the Laplacian image.

However, as In (c) it shows that this approach doubles the thickness
of the lines.

A more suitable approach is to use the positive values of the
Laplacian image.

As in (d) this approach results in thinner lines, which are
considerably more useful.

Edge detection is the approach used most frequently for
segmenting images based on abrupt (local) changes in
intensity.

Edge models are classified according to their intensity
profiles:

A
step edge
involves a transition between two intensity levels
occurring ideally over the distance of
1
pixel. For example in
images generated by a computer for use in areas such as
solid modeling and animation.

These clean, ideal edges can
occur over the distance of
1
processing (such as smoothing) is used to make them look
“real”.

Digital Step edges :
are used frequently as edge models in an
algorithm development. For example, the canny edge
detection algorithm was derived using a step
-
edge model.

In practice, digital images have edges that are blurred and
noisy, with the degree of blurring determined by the
limitation in the focusing mechanism (e.g., lenses in the case
of optical images). In such situation. Edges are more closely
modeled as having an intensity ramp profile.

A third model of an edge is the so
-
called
roof edge
, having
the characteristics illustrated in fig.
10.8
(c) . Roof edges are
models of lines through a region, with the base (width) of the
roof edge being determined by the thickness and sharpness
of the line.

We conclude the edge models section by noting the there are
three fundamental steps performed in edge detection:

1
.
Image Smoothing for noise reduction
: the need of this step
is to reduce the noise in an image.

2
.
Detection of edge points
: it is a local operation that
extracts all points from an image, these points are potential
candidates to become edge points.

3
.
Edge Localization:
the objective of this point is to select
from edge points only the points that are true members of
the set of points comprising an edge.

In this section the images are partitioned directly into
regions based on intensity values.

The basics of intensity
thresholding
:

Suppose that the intensity histogram in Fig.
10.35
(a) corresponds to an
image, f(
x,y
), composed of light objects on a dark background, in such a
way that object and background pixels have intensity values grouped into
two dominant modes. One obvious way to extract the objects from the
background is to select a threshold, T, that separates these modes. Then,
any point (
x,y
) in the image at which f(
x,y
) >T is called an object point;
otherwise, the point is called a background point.

Separating touching objects in an image is one of the more
difficult image processing operations.

The watershed transform is often applied to this problem.

The watershed transform finds "watershed ridge lines" in an
image by treating it as a surface where light pixels are high
and dark pixels are low.

Segmentation using the watershed transform works better if
you can identify, or "mark," foreground objects and
background locations

Refer to:
http://www.mathworks.com/products/image/demos.html?fil
e=/products/demos/shipping/images/ipexwatershed.html