Digital image processing

Fundamentals steps for development
Juan Guedes Pereira
Digital Image Processing – Juan Guedes Pereira

Background
 Why process image?
Basic concepts
 What you need to know?
Fundamental steps
 A methodology of project.
Conclusion

Interest in digital image processing method derives
two principal application areas:
Improvement of visual
information for human
interpretation
Autonomous machine
perception &
industrial process

Improvement of visual information for human
interpretation.

Autonomous machine perception.

One of the first applications was in improving
digitized newspaper pictures sent by submarine cable
between London and New York.

Advents of ...
large-scale digital computers
space program
Brought into focus the potential of image processing concepts.

Image processing has been used to solve a bunch of
problems.
WEG.23
Industrial machine vision
Processing of fingerprints
Biomedical analysis
Geographical mapping

Image processing has been used to solve a bunch of
problems.

The term monochrome image refers to a two-
dimensional light intensity function f(x,y).
x and y denote spatial coordinates
Value of f is proportional to the brightness

A digital image can be considered a matrix whose
row and column indices indentify a point in the
image and the corresponding matrix element value
indentifies the gray level at that point.

It’s very important for human comprehension a way
to model an image color.
The most applied it is the RGB model.

Images represented in the RGB color model consist of
three component images – one for each primary
color.

The transform theory.
The Fourier transform
decomposes functions into
its constituent frequencies;
Highlights some
characteristics.

Digital image processing includes a broad range of
hardware, software and theory.

To improve you chance of success…
ACQUISITION
PREPROCESSING
SEGMENTATION
REPRESENTATION
&
DESCRIPTION
RECOGNITION
&
INTERPRETATION
KNOWLEDGE BASE
POSTPROCESSING
PROBLEM
DOMAIN
RESULT

The problem domain is defined as the subject to be
process.
This domain has the characteristics that will define
the knowledge base.
It contains, in somehow, the result that you are
looking for.

Requires an image sensor and the capability to
digitize the signal produced by the sensor.
This sensor could be a monochrome or color TV
camera.
Produces an entire image of the problem
domain in rate of some frames per seconds.

Requires an image sensor and the capability to
digitize the signal produced by the sensor.
The sensor could also be an x-ray camera.
Produces by reflecting in some parts of an
object a 2-D image.

The result has to be more suitable than the original
one for a specific application.

There are two approaches for image enhancement,
the special domain methods and the frequency
domain methods.
Special domain: Sobel filter
frequency domain: low pass filter

The following steps deals with techniques for
extracting information, we refer to this area as image
analysis.

Segmentation is defined as partitions an input image
into its constituent parts or object.
In general, autonomous segmentation is one of the
most difficult tasks in digital processing.

The best way to segment an image is to detect its
discontinuities.
Dots
Lines
Edges
These three uses mathematical function as operator,
such as gradient and laplacian functions

During the thresholding process, pixels in an image
are marked as "object" pixels if their value is greater
than some threshold value.
The value histogram could be:
 Gray level;
 Color intensity;
 Others values.
The threshold value also could be
 intensity average;
 Median of a value.

A segmented region can be represented by boundary
pixels or internal pixels.
When shape is important, a boundary (external)
representation is used.

A segmented region can be represented by boundary
pixels or internal pixels.
When color or texture is important, an internal
representation is applied.

The next task is to describe the region based on the
chosen representation.
For internal representation :
 Average;
 Standard deviation;
 Moment;
 Entropy;
 …

The next task is to describe the region based on the
chosen representation.
For boundary:
 Diameter;
 Area;
 Perimeter
 Major axis;
 …

The last stage involves recognition and
interpretation.
Recognition is the process the assigns a label to an
object based on the information provided by its
descriptors.
Major axis = 2,3 cm
# of holes = 2
Hole #2 area = 25 mm2
Letter g

Different methods to recognize an image.
Pattern matching
Neural networks

Interpretation involves assigning meaning to an
ensemble of recognizes object.
Image analysis tasks can be as simple as…
or as sophisticated as…
reading bar coded tags
identifying a person from their face

This interpretation requires a bunch of logical test
and rules, which defines and, finally, gave meaning to
the process.
Methods for discovering relations between variables.
If ( object == “n” and followed by object == “o” )
Then means = no.

To process a image is becoming cheaper and easier;
Anyone has access to a video camera;
Software for image enhancement are as common as
text editors;

Following that methodology of image processing
increase your success probability;
The most difficult task is to transfer our recognition
and interpretation of an object to machine language.

How can we distinguish a scissor of a pliers?

Juan Guedes Pereira
jgp@neo.ufsc.br
www.facebook.com/juangp3
www.twitter.com/juangp3
www.neo.ufsc.br

Digital image processing

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