fuzzy image processing

Fuzzy image processing
SUPERVISOR:
ENSAF AL ZORQA

----The most important topics--
--

What is an image?
An image refers to a 2D light intensity function f(X,Y)
denote spatial coordinates and the value of f at any
point (X,Y) is proportional to the brightness or gray
levels of the image at that points.

Image Processing
Types of image:
1-Binary Image: It is the values of the image is zeros or
ones and either black or white.
Can be referred to within the binary image(1 bit per pixel).
It can convert all kinds of pictures into a binary image by
Threshold

2-Gray _Scale _Image: This represents the kind of
images on the basis of a single color of the image or the
so-called (Monochrome).It has information just about
lighting not about colors of image
Image Processing

3-Color Image : Color image has a specific model
consists of (3 Band) every Band represent one color
and every color referred to it's by specific
representation it is red- green-blue,(RGB) . Each color
takes 8-bit , So each color image has a 24-bit.
Image Processing

4-Multi spatial Image: it's consist of Gradients of colors .
Image Processing
What is a Digital Image?
A digital image is a representation of a two-dimensional
image as a finite set of digital values, called picture elements
or pixels, Pixel values typically represent gray levels, colors,
heights, opacities etc..

Remember digitization implies that a digital image is an
approximation of a real scene.
Image Processing

Image Processing
Set of computational techniques for analyzing, enhancing,
compressing, and reconstructing image.
Image Processing

The applications of image processing :
Image Processing
1- news- paper industry

2- in computing technology
Image Processing

3- in medical applications
Image Processing

4-In Image enhancement/restoration
Image Processing

4- Artistic effects
Image Processing

5-Industrial inspection
Image Processing

6- Law enforcement
Image Processing

7-Human computer interfaces
Image Processing

step of image processing:-
1-Image acquisition: to acquire a digital image.
2-Image Pre-processing: to improve the image in ways that
increase the chances for success of the other process.
3-Image Segmentation: to partition an input image in to its
constituent parts or objects.
4-Image Representation: to convert the input data to a form
suitable for computer processing.
Image Processing

5-Image Description: to extract features that results in some
quantitative information of interest or features that are basic
for differentiating one class of objects from another.
6-Image Recognition: to assign a label to an object based on
the information provided by its description.
7-Image Interpretation: to assign meaning to an ensemble of
recognized objects.
Image Processing

The major topics within the field of image processing
include:
1-Image restoration:
The purpose of image restoration is to "compensate for"
or "undo" defects which degrade an image.
Degradation comes in many forms such as motion blur,
noise.
Image Processing

a. Image with distortion b. Restored image
Image Processing

Image Processing
There are many methods used in the image
processing world to restore images:
1-Inverse Filtering
2-blind deconvolution
3-Wavelet-based Image Restoration
4-Wiener Filtering

Image Processing
2-Image enhancement:
Involves taking an image and improving it visually, typically
by taking advantages of human Visual Systems responses. One
of the simplest enhancement techniques is to simply stretch the
contrast of an image.

Image Processing
a. image with poor contrast b. Image enhancement by
contrast stretching

Image Processing
The enhancement methods can broadly be divided in to the
following two categories:
1-Spatial Domain Methods:- we directly deal with the image
pixels.
The pixel values are manipulated to achieve desired
enhancement.
2-Frequency Domain Method:- the image is first transferred in
to frequency domain. It means that, the Fourier Transform of
the image is computed first.

Image Processing
3-Image compression:
Involves reducing the typically massive amount of data
needed to represent an image.
This done by eliminating data that are visually unnecessary
and by taking advantage of the redundancy that is inherent in
most images.

Image Processing
a. Image before compression b. Image after compression

All the science was meant to benefit the people in their
lives, and more precisely in solving their problems of life
by simplifying the problem and deal with it then analyzed
to reach the appropriate solutions.
It was a ways to solve problems, how to express
expressions that describe the amounts of stuff,
expressions that are ambiguous, or somewhat similar,
preoccupy scientists and specialists.
Overview

What color is this leopard?
Overview

Is this glass full or empty?
Overview

A little hot air.
Also varies depending on
the expression of the
atmosphere of the person
and his place of residence ..
temperature of 20 Celsius he
may judge Gulf population
cool While he sees the
population of Scandinavia hot.
Fuzzy logic

The previous example we use a lot in our daily lives, but
if we come to the process of converting or translating
these sentences to specific software for processing, we
will see that programmers will suffer from how to deal
with these sentences
Fuzzy logic

-From the above examples it is clear the presence of
gradients of any expression .. Hot air, cold, moderate,
relatively cool, relatively hot.
-So the problem is how to express what degree of
belonging to the description described it, such as
temperature, length, weight, speed, intelligence and
beauty .... Etc.
Fuzzy logic

The fuzzy logic is a form of logic,
is used in some expert systems
and applications of artificial
intelligence, originated this
logic in 1965 by scientist
Azerbaijani origin, "Lotfi Zadeh,“
University of California
but the theory did not receive
attention until 1974.
History of Fuzzy logic

where he noted that right and wrong are not enough to
represent all forms of logical and especially the problems
that confront us currently.

The Classical logic (Boolean) depends on only 0 or 1,
and this depends upon a lot of relationships, while there
are other relationships where the position that it can be
considered partly true or partly wrong at the same time.

definitions of fuzzy logic
is a technique through which determine the degree of
affiliation or degree of health, which is the extent of
grades between right and wrong, and this is the
difference between him and Boolean logic, which only
knows right and wrong (True - False).
Fuzzy logic

Fuzzy Sets :-
- Any set of elements or objects with which it has a
relationship with each other and belong to the same
definition called the set . The contents of the set here
called scientifically elements or members.
-It must here to know that the set and its members must
be available by the following conditions:
1- non-recurring.
2-Be clear in terms of the relationship that linked to
each other.
Fuzzy Sets

Suppose we wanted to buy a sports car ... the car will be evaluated
by the maximum speed
Fuzzy Sets

So we will create a standard or the following law:-
( which exceed the car a top speed of 280 km / h are fast) and
symbolizes the requirement for speed sports car (μ),
The following graph illustrates the selection process ..
Fuzzy Sets

As it turns out the picture above:-
1- May draw the line between cars proposed the so-called
status of sharp edged membership functions, and including
means the process of separation between the two parts fully.
2- The maximum speed of the car, if they exceed the 280 it is
fast.
What is classified if the car speed of 270 or 279??
According to the law, and the drawing shown above, the car is
slow because it did not exceed the maximum speed of
280 km / h.
Fuzzy logic

* If we have a problematic with the speed one unit
* In theory, the speed there is no difference between a car
that exceeded the 280, which exceeded the 270 and even the
240 they are all fast cars because the difference is very simple
* To resolve this issue has been the use of fuzzy set theory,
which knew how to differentiate between the two cultivars in
a satisfactory manner, depending on how the process of
representation fuzzy set for these cases is much better than
using the usual sports groups, they contain values and
sequential gradient between zero and one
Fuzzy logic

I meant between zero and one is between acceptable
and unacceptable True or False, there is no barrier
between the two cultivars, but it will be graded
between car values that exceed a top speed of between
280 and a top speed in excess of 246, for example.
Here would be a fair division process, as we see in the
following image:
Fuzzy logic

In the picture above we found out something very important in the
principle of uncertainty, which is a function of belonging, which means
that the speed of 270, the proportion of affiliation to the required speed is
95%, which is better than the speed of 260 that the ratio of affiliation, for
example, 85%, and this could be the car that speed within 270 fast cars
because it has a higher percentage than other cars here have been proposed
to solve the problem the process of developing a sequential ratios between
the requirement for acceptance and rejection, or between the value of right
and wrong.
Fuzzy logic

Definitions Fuzzy set:-
As defined by dr. Lotfi zadeh, in his famous publication in
1965, is a set of objects that possess valuable sequential
and graded for affiliation to those group.
Fuzzy logic

What is the function of belonging Membership
Function?
Is the function that determines the proportion of the
element belonging to that fuzzy set.
Fuzzy logic

Fuzzy logic
Elementvaluesbelongingtothe
group
value of the item

Applications of fuzzy logic in practical life
1-artificial intelligence
2-operational control
Some applications are as follows:
Video cameras
Sensing the movement of things
Fuzzy logic

Cars
Possibility of controlling
the speed
Air conditioning
Gradually reducing
the heat
Fuzzy logic

Using fuzzy logic in image processing
- fuzzy logic aims to model the vagueness and
ambiguity in complex systems
- In recent years the concept of fuzzy logic has been
extended to image processing by Hamid Tizhoosh.

Fuzzy image processing is not a unique theory .
It is a collection of different fuzzy approaches to image
processing.
The representation and processing depend on the selected
fuzzy technique and on the problem to be solved.

Fuzzy image processing is divided into three main
stages:
image fuzzification .
modification of membership values.
image defuzzification.

we encode image data (fuzzification) and decode the
results (defuzzification) to process images by means of
fuzzy techniques.
-The main power of fuzzy image processing is mainly in the
intermediate step, a change of membership function values.

Fig.2. Steps of fuzzy image processing

The Advantages of Fuzzy Image Processing
There are many reasons to use Fuzzy technique. The most
important of them are as follows:
Fuzzy techniques are powerful tools for knowledge representation
and processing.
 Fuzzy techniques can manage the vagueness and ambiguity
efficiently.
• Fuzzy image processing

three other kinds of imperfection in the image
processing:-
● Grayness ambiguity
● Geometrical fuzziness
● Vague (complex/ill-defined) knowledge

Fig.3. Uncertainty/imperfect knowledge in image processing

-“There are many classical thresholding techniques used in
image processing,” says Tizhoosh
-and recently the concept of image fuzziness has been used to
develop new thresholding techniques.
For example, a standard S membership function can be moved
pixel by pixel over the existing range of gray levels and in
each position, a measure of fuzziness calculated.

Tizhoosh distinguishes between the following kinds of image
fuzzification:
1.Histogram-based gray-level fuzzification (or briefly
histogram fuzzification)
Example: brightness in image enhancement
2.Local fuzzification (Example: edge detection)
3.Feature fuzzification (Scene analysis, object recognition)

Theory of Fuzzy Image Processing
the most important theoretical components of fuzzy image
processing:
# Fuzzy Geometry (Metric, topology, ...)
# Measures of Fuzziness and Image Information (entropy,
correlation, divergence, expected values, ...)
# Fuzzy Inference Systems (image fuzzification, inference,
image defuzzification)
# Fuzzy Clustering (Fuzzy c-means, possibilistic c-means,
...)

# Fuzzy Mathematical Morphology (Fuzzy ersion, fuzzy
dilation, ...)
# Fuzzy Measure Theory (Sugeno measure/integral, possibility
measures, necessity measures,...)
# Fuzzy Grammars
# CombinedApproaches (Neural fuzzy/fuzzy neural
approaches, fuzzy genetic algorithms, fuzzy wavelet analysis)
# Extension of classical methods (Fuzzy Hough transform,
fuzzy median filtering, ...)

Applications of Fuzzy Logic in Image Processing
Noise Detection and Removal
Edge Detection
Segmentation
Contrast Enhancement
Geometric measurement
Scene analysis (Region Labeling)

Fuzzy filter
-The general idea behind the filter is to average a pixel
using other pixel values from its neighborhood, but
simultaneously to take care of important image structures
such as edges.1.
-The main concern of the proposed filter is to distinguish
between local variations due to noise and due to image
structure.

Noise Reduction by Fuzzy Image Filtering
The filter consists of two stages:-
- The first stage computes a fuzzy derivative for eight
different directions.
- The second stage uses these fuzzy derivatives to perform
fuzzy smoothing by weighting the contributions of
neighboring pixel values.
Both stages are based on fuzzy rules which make use of
membership functions.Fuzzy techniques have already been
applied in several domains of image processing (e.g.,
filtering, interpolation , and morphology )

The further construction of the filter is then based on the
observation that
-a small fuzzy derivative most likely is caused by noise.
-while a large fuzzy derivative most likely is caused by an
edge in the image.

Color Image Noise Reduction Using Fuzzy Filtering
( N. Baker et al., 2008)

Edge detection :-
is an essential feature of digital image processing .It is
approach used most frequently in image segmentation
based on abrupt changes in intensity

Fuzzy Inference System
The new fuzzy rule based edge detection system is developed by designing a Fuzzy
Inference System (FIS) of type using MATLAB toolbox
The algorithm detects edges of an input image by using a window mask of 2x2 size
that slides over the whole image horizontally pixel by pixel.
- The FIS is implemented by considering four inputs which correspond to four pixels
P1, P2, P3 and P4 of the 2*2
mask in Figure-1 and one output variable
P2 x(I,1,j)P1 x(I,1,1)
P4 x(I,j)P3x(I,j,1)

Experiments
-The new approach developed for edge detection has been
tested for different images.
-The algorithm is able to detect very sharp and distinct edges
for different kind of images.
-Some sample images are shown in Figure 5(a) and 6(a).
-The algorithm is also tested with different image formats like
JPG, PNG, BMP and it works suitably with all these variety of
image formats.

Figure 5(a): Sample Image -1 Figure 5(b): Edges detected by
the algorithm

Figure 6(a): Sample Image-2 Figure 6(b): Edges detected by the
algorithm

SEGMENTATION USING FUZZY LOGIC The following points give a brief
overview of different fuzzy approaches to image segmentation:-
1. Fuzzy Clustering:
Algorithms Fuzzy clustering is the oldest fuzzy approach to image segmentation.
Algorithms such as fuzzy c-means (FCM) can be used to build clusters (segments).
The class membership of pixels can be interpreted as similarity or compatibility with
an ideal object or a certain property.

2. Fuzzy Rule-Based Approach:
If we interpret the image features as linguistic variables, then
we can use fuzzy if-then rules to segment the image into different regions. A simple
fuzzy segmentation rule may seem as follows: IF the pixel is dark AND its
neighbourhood is also dark AND homogeneous THEN it belongs to the background.
3.Fuzzy Integrals:
- Fuzzy integrals can be used in different forms: Segmentation by weightening the
features (fuzzy measures represent the importance of particular features).
- Segmentation by fusion of different sensors (e.g. multi spectral images)

4.Measures of Fuzziness and image:
Measures of fuzziness (e.g. fuzzy entropy) and image information
(e.g.fuzzy divergence) can be also used in segmentation and
thresholding tasks.
5.Fuzzy Geometry:
Fuzzy geometrical measures such as fuzzy compactness and index of
area coverage can be used to measure the geometrical fuzziness of
different regions of an image.

Thank You For Your Attention

fuzzy image processing

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