2. INTRODUCTION
Segmentation divides an image into its constituent regions or
objects.
Segmentation allows to extract objects in images.
Segmentation Should Stop when the objects of interest in an
application has been solved.
EX: Automated inspection of electronic such as missing component
or broken path
Image Segmentation algorithm based on property of intensity
values :Discontinuity and Similarity
Discontinuity: partition based on abrupt change in intensity
Similarity: Partition of image based on predefined criteria
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3. 3
Applications of image segmentation include
Identifying objects in a scene for object-
based measurements such as size and
shape
-Identifying objects in a moving scene for
object-based video compression (MPEG4)
-Identifying objects which are at different
distances from a sensor using depth
measurements from a laser range finder
enabling path planning for a mobile robots
4. 4
Segmentation Based on Grey Scale
-Very simple ‘model’ of grey scale leads to inaccuracies
in object labelling
Grey Scale Image
Segmented Image
6. 6
-The main difficulty of motion segmentation is that an
intermediate step is required to (either implicitly or
explicitly) estimate an optical flow field
-The segmentation must be based on this estimate and
not, in general, the true flow
Segmentation based on motion
7. 7
-This example shows a range image, obtained with
a laser range finder
-A segmentation based on the range (the object
distance from the sensor) is useful in guiding
mobile robots
Segmentation based on depth
Original
image
Segmented
Image
8. 8
-TWO VERY SIMPLE IMAGE SEGMENTATION TECHNIQUES THAT
ARE BASED ON THE GREYLEVEL HISTOGRAM OF AN IMAGE IS
-THRESHOLDING
-CLUSTERING
WE CAN CONSIDER THE HISTOGRAMS OF OUR IMAGES
FOR THE NOISE FREE IMAGE, ITS SIMPLY TWO SPIKES AT
I=100, I=150
FOR THE LOW NOISE IMAGE, THERE ARE TWO CLEAR PEAKS
CENTRED ON I=100, I=150
FOR THE HIGH NOISE IMAGE, THERE IS A SINGLE PEAK –
Grey Level Histogram based Segmentation
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0.00 50.00 100.00 150.00 200.00 250.00
i
h(i)
Noise free
Low noise
Highnoise
9. 9
THERE ARE THREE BASIC TYPES OF GRAY-LEVEL
DISCONTINUITIES IN A DIGITAL IMAGE: POINTS, LINES, AND
EDGES
THE MOST COMMON WAY TO LOOK FOR DISCONTINUITIES
IS TO RUN A MASK THROUGH THE IMAGE.
WE SAY THAT A POINT, LINE, AND EDGE HAS BEEN
DETECTED AT THE LOCATION ON WHICH THE MASK IS
CENTERED IF R>T
Edge-based segmentation
10. 10
Point Detection
Point detection can be
achieved simply using the
mask below:
Points are detected at
those pixels in the
subsequent filtered image
that are above a set
threshold
11. 11
Line Detection
The next level of complexity is to try to detect lines
The masks below will extract lines that are one pixel thick
and running in a particular direction
12. 12
EDGE DETECTION
An edge is a set of connected pixels that lie on the
boundary between two regions
13. 13
EDGE DETECTION
Edge detection: Gradient operation
x
y
f
G x
G f
y
f
1
2 2 2
( ) x yf mag f G G
1
( , ) tan ( )y
x
G
x y
G
1st derivative tells us
where an edge is
2nd derivative can
be used to show
edge direction
14. 14
REGION-BASED SEGMENTATION
Region growing: Groups pixels or sub-region into
larger regions.
step1:
Start with a set of “seed” points and from these grow regions by
appending to each seed those neighboring pixels that have properties
similar to the seed.
step2:
Region splitting and merging
Advantage Disadvantage
With good connectivity Initial seed-points:
different sets of initial seed-point
cause different segmented result
Time-consuming problem
15. 15
THRESHOLDING
Thresholding is usually the first step in any
segmentation approach
Single value thresholding can be given
mathematically as follows:
Tyxfif
Tyxfif
yxg
),(0
),(1
),(
Tyxfif
Tyxfif
yxg
),(0
),(1
),(
16. 16
Based on the histogram of an image
Partition the image histogram using a single global
threshold
The success of this technique very strongly depends on
how well the histogram can be partitioned
Original Image Thresholded Image
17. 17
THRESHOLD
If you get the threshold wrong the results can be disastrous
Threshold Too Low Threshold Too High
18. 18
THRESHOLDING ALGORITHM
The basic global threshold, T, is calculated
as follows:
1. Select an initial estimate for T (typically the average grey level in the
image)
2. Segment the image using T to produce two groups of pixels: G1
consisting of pixels with grey levels >T and G2 consisting pixels with
grey levels ≤ T
3. Compute the average grey levels of pixels in G1 to give μ1 and G2 to give
μ2
4. Compute a new threshold value:
5. Repeat steps 2 – 4 until the difference in T in successive iterations is
less than a predefined limit T∞
This algorithm works very well for finding thresholds when the histogram is suitable
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21
T
19. 19
An approach to handling situations in which
single value thresholding will not work is to
divide an image into sub images and
threshold these individually
since the threshold for each pixel depends on
its location within an image this technique is
said to adaptive
Basic Adaptive Thresholding
20. 20
BASIC ADAPTIVE THRESHOLDING EXAMPLE
The image below shows an example of using adaptive
thresholding with the image shown previously
As can be seen success is mixed
But, we can further subdivide the troublesome sub
images for more success
21. 21
BASIC ADAPTIVE THRESHOLDING EXAMPLE
These images show the
troublesome parts of the
previous problem further
subdivided
After this sub division
successful thresholding
can be
achieved
