Fundamentals of Image Processing & Components.ppt

1. Image Processing [KCS-062]

2. Unit-I
• Introduction: DIGITAL IMAGE FUNDAMENTALS: Steps in Digital Image Processing –
Components – Elements of Visual Perception – Image Sensing and Acquisition – Image
Sampling and Quantization – Relationships between pixels – Color image fundamentals – RGB,
HSI models, Two-dimensional mathematical preliminaries, 2D transforms – DFT, DCT.
Unit -II
• IMAGE ENHANCEMENT : Spatial Domain: Gray level transformations – Histogram processing
– Basics of Spatial Filtering– Smoothing and Sharpening Spatial Filtering, Frequency Domain:
Introduction to Fourier Transform– Smoothing and Sharpening frequency domain filters – Ideal,
Butterworth and Gaussian filters, Homomorphic filtering, Color image enhancement.
Unit - III
• IMAGE RESTORATION : Image Restoration – degradation model, Properties, Noise models –
Mean Filters – Order Statistics – Adaptive filters – Band reject Filters – Band pass Filters –
Notch Filters – Optimum Notch Filtering – Inverse Filtering – Wiener filtering
Unit - IV
• IMAGE SEGMENTATION: Edge detection, Edge linking via Hough transform – Thresholding –
Region based segmentation – Region growing – Region splitting and merging – Morphological
processing- erosion and dilation, Segmentation by morphological watersheds – basic concepts
– Dam construction – Watershed segmentation algorithm
Unit -V
• IMAGE COMPRESSION AND RECOGNITION: Need for data compression, Huffman, Run
Length Encoding, Shift codes, Arithmetic coding, JPEG standard, MPEG. Boundary
representation, Boundary description, Fourier Descriptor, Regional Descriptors – Topological
feature, Texture – Patterns and Pattern classes – Recognition based on matching.

5. 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

6. What is a digital image? (Cont.)
• Pixel values typically represent gray
levels, colours, heights, opacities etc
• Remember digitization implies that a
digital image is an approximation of a real
scene
1 pixel

7. What is Digital Image
Processing?
• Digital image processing focuses on two
major tasks
– Improvement of pictorial information for
human interpretation
– Processing of image data for storage,
transmission and representation for
autonomous machine perception

17. Uses of DIP
– Image enhancement/restoration
– Artistic effects
– Medical visualisation
– Law enforcement
– Human computer interfaces

18. Examples: Image Enhancement
• One of the most common uses of DIP
techniques: improve quality, remove noise etc

19. Examples: The Hubble
Telescope
•Launched in 1990 the Hubble
telescope can take images of
very distant objects
•However, an incorrect mirror
made many of Hubble’s
images useless
•Image processing
techniques were
used to fix this

20. Examples: Artistic Effects
• Artistic effects are used to make images
more visually appealing, to add special
effects and to make composite images

21. Examples: Medicine
Take slice from MRI (Magnetic Resounance Imaging) scan of a heart,
and find boundaries between types of tissue
– Image with gray levels representing tissue density
– Use a suitable filter to highlight edges

22. Examples: GIS
•Geographic Information Systems
– Digital image processing techniques are used
extensively to manipulate satellite imagery
– Terrain classification (‫)التضاريس‬
– Meteorology (‫الجوية‬ ‫)األرصاد‬

23. Examples: Law Enforcement
•Image processing techniques are used extensively by law enforcers
– Number plate recognition for speed cameras/automated toll
systems
– Fingerprint recognition

24. Examples: HCI
•Try to make human computer interfaces more natural
– Face recognition
– Gesture recognition
•Does anyone remember the
user interface from “Minority Report”?
•These tasks can be extremely difficult

25. Image Processing Fields
• Computer Graphics: The creation of
images
• Image Processing: Enhancement or
other manipulation of the image
• Computer Vision: Analysis of the image
content

26. Image Processing Fields
Input / Output Image Description
Image Image Processing Computer Vision
Description Computer
Graphics
AI
Sometimes, Image Processing is defined as “a
discipline in which both the input and output of a
process are images
But, according to this classification, trivial tasks of
computing the average intensity of an image would not
be considered an image processing operation

27. Computerized Processes Types
• Low-Level Processes:
– Input and output are images
– Tasks: Primitive operations, such as, image
processing to reduce noise, contrast enhancement
and image sharpening

28. Computerized Processes Types
• Mid-Level Processes:
– Inputs, generally, are images. Outputs are
attributes extracted from those images (edges,
contours, identity of individual objects)
– Tasks:
• Segmentation (partitioning an image into regions
or objects)
• Description of those objects to reduce them to a
form suitable for computer processing
• Classifications (recognition) of objects

29. Computerized Processes Types
• High-Level Processes:
– Image analysis and computer vision

30. Digital Image Definition
• An image can be defined as a two-
dimensional function f(x,y)
• x,y: Spatial coordinate
• F: the amplitude of any pair of coordinate
x,y, which is called the intensity or gray
level of the image at that point.
• X,y and f, are all finite and discrete
quantities.

31. Fundamental Steps in Digital Image Processing:
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement Representation
& Description
Problem Domain
Colour Image
Processing
Image
Compression
Wavelets &
Multiresolution
processing
Knowledge Base
Outputs of these processes generally are images
Outputs
of
these
processes
generally
are
image
attributes

32. Key Stages in Digital Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

33. Key Stages in Digital Image Processing:
Image Aquisition
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

34. Key Stages in Digital Image Processing:
Image Enhancement
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

35. Key Stages in Digital Image Processing:
Image Restoration
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

36. Key Stages in Digital Image Processing:
Morphological Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

37. Key Stages in Digital Image Processing:
Segmentation
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

38. Key Stages in Digital Image Processing:
Object Recognition
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

39. Key Stages in Digital Image Processing:
Representation & Description
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

40. Key Stages in Digital Image Processing:
Image Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

41. Key Stages in Digital Image Processing:
Colour Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

42. Fundamental Steps in DIP:
(Description)
Step 1: Image Acquisition
The image is captured by a sensor (eg.
Camera), and digitized if the output of the
camera or sensor is not already in digital
form, using analogue-to-digital convertor

43. Fundamental Steps in DIP:
(Description)
Step 2: Image Enhancement
The process of manipulating an image so that
the result is more suitable than the original for
specific applications.
The idea behind enhancement techniques is
to bring out details that are hidden, or simple
to highlight certain features of interest in an
image.

44. Fundamental Steps in DIP:
(Description)
Step 3: Image Restoration
- Improving the appearance of an image
- Tend to be mathematical or probabilistic
models. Enhancement, on the other hand, is
based on human subjective preferences
regarding what constitutes a “good”
enhancement result.

45. Fundamental Steps in DIP:
(Description)
Step 4: Colour Image Processing
Use the colour of the image to extract
features of interest in an image

46. Fundamental Steps in DIP:
(Description)
Step 5: Wavelets
Are the foundation of representing images
in various degrees of resolution. It is used
for image data compression.

47. Fundamental Steps in DIP:
(Description)
Step 6: Compression
Techniques for reducing the storage
required to save an image or the
bandwidth required to transmit it.

48. Fundamental Steps in DIP:
(Description)
Step 7: Morphological Processing
Tools for extracting image components
that are useful in the representation and
description of shape.
In this step, there would be a transition
from processes that output images, to
processes that output image attributes.

49. Fundamental Steps in DIP:
(Description)
Step 8: Image Segmentation
Segmentation procedures partition an image into its constituent
parts or objects.
Important Tip: The more accurate the segmentation, the more
likely recognition is to succeed.

50. Fundamental Steps in DIP:
(Description)
Step 9: Representation and Description
- Representation: Make a decision whether the data
should be represented as a boundary or as a complete
region. It is almost always follows the output of a
segmentation stage.
- Boundary Representation: Focus on external
shape characteristics, such as corners and
inflections
- Region Representation: Focus on internal
properties, such as texture or skeleton shape

51. Fundamental Steps in DIP:
(Description)
Step 9: Representation and Description
- Choosing a representation is only part of the solution
for transforming raw data into a form suitable for
subsequent computer processing (mainly recognition)
- Description: also called, feature
selection, deals with extracting attributes
that result in some information of interest.

52. Fundamental Steps in DIP:
(Description)
Step 9: Recognition and Interpretation
Recognition: the process that assigns
label to an object based on the information
provided by its description.

53. Fundamental Steps in DIP:
(Description)
Step 10: Knowledge Base
Knowledge about a problem domain is
coded into an image processing system in
the form of a knowledge database.

54. Components of an Image
Processing System
Network
Image displays Computer Mass storage
Hardcopy
Specialized image
processing
hardware
Image processing
software
Image sensors
Problem Domain
Typical general-
purpose DIP
system

55. Components of an Image
Processing System
1. Image Sensors
Two elements are required to acquire
digital images. The first is the physical
device that is sensitive to the energy
radiated by the object we wish to image
(Sensor). The second, called a digitizer,
is a device for converting the output of the
physical sensing device into digital form.

56. Components of an Image
Processing System
2. Specialized Image Processing
Hardware
Usually consists of the digitizer, mentioned before, plus
hardware that performs other primitive operations, such as
an arithmetic logic unit (ALU), which performs arithmetic
and logical operations in parallel on entire images.
This type of hardware sometimes is called a front-end
subsystem, and its most distinguishing characteristic is
speed. In other words, this unit performs functions that
require fast data throughputs that the typical main
computer cannot handle.

57. Components of an Image
Processing System
3. Computer
The computer in an image processing system is a
general-purpose computer and can range from a PC to a
supercomputer. In dedicated applications, sometimes
specially designed computers are used to achieve a
required level of performance.

58. Components of an Image
Processing System
4. Image Processing Software
Software for image processing consists of specialized
modules that perform specific tasks. A well-designed
package also includes the capability for the user to write
code that, as a minimum, utilizes the specialized modules.

59. Components of an Image
Processing System
5. Mass Storage Capability
Mass storage capability is a must in a image processing
applications. And image of sized 1024 * 1024 pixels
requires one megabyte of storage space if the image is
not compressed.
Digital storage for image processing applications falls into
three principal categories:
1. Short-term storage for use during processing.
2. on line storage for relatively fast recall
3. Archival storage, characterized by infrequent access

60. Components of an Image
Processing System
5. Mass Storage Capability
One method of providing short-term storage is computer memory.
Another is by specialized boards, called frame buffers, that store
one or more images and can be accessed rapidly.
The on-line storage method, allows virtually instantaneous image
zoom, as well as scroll (vertical shifts) and pan (horizontal shifts).
On-line storage generally takes the form of magnetic disks and
optical-media storage. The key factor characterizing on-line
storage is frequent access to the stored data.
Finally, archival storage is characterized by massive storage
requirements but infrequent need for access.

61. Components of an Image
Processing System
6. Image Displays
The displays in use today are mainly
color (preferably flat screen) TV monitors.
Monitors are driven by the outputs of the
image and graphics display cards that are
an integral part of a computer system.

62. Components of an Image
Processing System
7. Hardcopy devices
Used for recording images, include laser
printers, film cameras, heat-sensitive
devices, inkjet units and digital units,
such as optical and CD-Rom disks.

63. Components of an Image
Processing System
8. Networking
Is almost a default function in any computer
system, in use today. Because of the large
amount of data inherent in image processing
applications the key consideration in image
transmission is bandwidth.
In dedicated networks, this typically is not a
problem, but communications with remote sites
via the internet are not always as efficient.

64. THANK YOU