Image Processing and Pattern IPPR_Lecture9.ppt

Digital Image Processing
Image Segmentation

2
of
47
The Segmentation Problem

3
of
47
The Segmentation Problem
Segmentation attempts to partition the pixels
of an image into groups that strongly
correlate with the objects in an image
Typically the first step in any automated
computer vision application

4
of
47
Segmentation Examples
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

5
of
47
Image Segmentation
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

6
of
47
Image Segmentation
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

7
of
47
Detection Of Discontinuities
There are three basic types of grey level
discontinuities that we tend to look for in
digital images:
– Points
– Lines
– Edges
We typically find discontinuities using masks
and correlation

8
of
47

9
of
47

10
of
47
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
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

11
of
47
Point Detection (cont…)
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)
X-ray image of
a turbine blade
Result of point
detection
Result of
thresholding

12
of
47
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
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

13
of
47
Line Detection
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

14
of
47
Line Detection (cont…)
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)
Binary image of a wire
bond mask
After
processing
with -45° line
detector
Result of
thresholding
filtering result

15
of
47
Edge Detection
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

16
of
47
Edge Detection
An edge is a set of connected pixels that lie
on the boundary between two regions
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

17
of
47
Derivative Operators
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

18
of
47
Edges & Derivatives
We have already spoken
about how derivatives
are used to find
discontinuities
1st derivative tells us
where an edge is
2nd derivative can
be used to show
edge direction
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

19
of
47
Derivatives & Noise
Derivative based edge detectors are
extremely sensitive to noise
We need to keep this in mind
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

20
of
47
Common Edge Detectors
Given a 3*3 region of an image the following
edge detection filters can be used
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

21
of
47
Edge Detection Example
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)
Original Image Horizontal Gradient Component
Vertical Gradient Component Combined Edge Image

22
of
47
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

23
of
47
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

24
of
47
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

25
of
47
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

26
of
47
Laplacian Edge Detection
We encountered the 2nd-order derivative
based Laplacian filter already
The Laplacian is typically not used by itself
as it is too sensitive to noise
Usually when used for edge detection the
Laplacian is combined with a smoothing
Gaussian filter
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

27
of
47
Laplacian Edge Detection
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

28
of
47
Edge Linking and boundary
detection

29
of
47
Edge Linking : local processing

30
of
47

31
of
47

32
of
47
Summary
In this lecture we have begun looking at
segmentation, and in particular edge detection
Edge detection is massively important as it is
in many cases the first step to object
recognition

Image Processing and Pattern IPPR_Lecture9.ppt

More Related Content

Similar to Image Processing and Pattern IPPR_Lecture9.ppt

More from ssuserf35ac9

Recently uploaded

Image Processing and Pattern IPPR_Lecture9.ppt

Editor's Notes