image introduction and origin steps in DIP

DIP

1. Digital Image Fundamentals
 
Introduction – Fundamental Steps in Digital Image
Processing –Components of an Image Processing System,
Elements of Visual Perception – Image Sensing and
Acquisition – Image Sampling and Quantization – RGB and
HSI color models.

2. Contents
This lecture will cover:
–What is a digital image?
–What is digital image processing?
–History of digital image processing
–State of the art examples of digital image
processing
–Key stages in digital image processing

4. What is an Image?
•An image is a 2D rectilinear array of pixels
Continuous image Digital image

5. What is an Image?
•An image is a 2D rectilinear array of pixels
Continuous image Digital image
A pixel is a sample, not a little square!

6. What is an Image?
•An image is a 2D rectilinear array of pixels
A pixel is a sample, not a little square!
Continuous image Digital image

7. What are images?
•An image is a 2-d rectilinear array of pixels

8. Pixels as samples
•A pixel is a sample of a continuous function

9. Digital Image
Digital image = a multidimensional
array of numbers (such as intensity image)
or vectors (such as color image)
Each component in the image
called pixel associates with
the pixel value (a single number in
the case of intensity images or a
vector in the case of color images).
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10. Common image formats include:
–1 sample per point (B&W or Grayscale)
–3 samples per point (Red, Green, and Blue)
–4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a.
Opacity)
For most of this course we will focus on grey-scale
images

14. 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.

15. How it works
In the above figure , an image has been captured by a camera and has
been sent to a digital system to remove all the other details , and just
focus on the water drop by zooming it in such a way that the quality of
the image remains the same.

19. History of Digital Image Processing
Early
1920s:
One of the first applications of
digital imaging was in the news-
paper industry
–The Bartlane cable picture
transmission service
–Images were transferred by submarine cable
between London and New York
–Pictures were coded for cable transfer and
reconstructed at the receiving end on a
telegraph printer
Early digital image

20. History of DIP (cont…)
Mid
to late 1920s:
Improvements to the
Bartlane system resulted in higher quality
images
–New reproduction
processes based
on photographic
techniques
–Increased number
of tones in
reproduced images
Improved
digital imageEarly 15 tone digital image

21. History of DIP (cont…)
1960s: Improvements in computing
technology and the onset of the space race
led to a surge of work in digital image
processing
–1964:

Computers used to
improve the quality of
images of the moon taken
by the Ranger 7 probe
–Such techniques were used
in other space missions
including the Apollo landings
A picture of the moon taken by
the Ranger 7 probe minutes
before landing

22. History of DIP (cont…)
1970s: Digital image processing begins to be
used in medical applications
–1979: Sir Godfrey N.
Hounsfield & Prof. Allan M.
Cormack share the Nobel
Prize in medicine for the
invention of tomography,
the technology behind
Computerised Axial
Tomography (CAT) scans
Typical head slice CAT image

23. History of DIP (cont…)
1980s
- Today:
The use of digital image processing
techniques has exploded and they are now used for
all kinds of tasks in all kinds of areas
–Image enhancement/restoration
–Artistic effects
–Medical visualisation
–Industrial inspection
–Law enforcement
–Human computer interfaces

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

25. 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

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

27. Examples: Medicine
Take slice from MRI scan of canine heart, and find
boundaries between types of tissue
–Image with gray levels representing tissue
density
–Use a suitable filter to highlight edges
Original MRI Image of a Dog Heart
Edge Detection Image

28. Examples:
GIS
Geographic Information Systems
–Digital image processing techniques are used
extensively to manipulate satellite imagery
–Terrain classification
–Meteorology

29. Examples: GIS (cont…)
Night-Time Lights of
the World data set
–Global inventory of
human settlement
–Not hard to imagine
the kind of analysis
that might be done
using this data

30. Examples: Industrial Inspection
Human operators are
expensive, slow and
unreliable
Make machines do the
job instead
Industrial vision systems
are used in all kinds of
industries
Can we trust them?

31. Examples: PCB Inspection
Printed Circuit Board (PCB) inspection
–Machine inspection is used to determine that
all components are present and that all
solder joints are acceptable
–Both conventional imaging and x-ray imaging
are used

32. Examples: Law Enforcement
Image processing
techniques are used
extensively by law
enforcers
–Number plate
recognition for speed
cameras/automated
toll systems
–Fingerprint recognition
–Enhancement of CCTV
images

33. 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

34. Applications
of Digital Image
Processing
•Image sharpening and restoration
•Medical field
•Remote sensing
•Transmission and encoding
•Machine/Robot vision
•Color processing
•Pattern recognition
•Video processing
•Microscopic Imaging
•Others

35. Image sharpening and restoration
•Image sharpening and restoration refers here to process images that
have been captured from the modern camera to make them a better
image or to manipulate those images in way to achieve desired
result. It refers to do what Photoshop usually does.
•This includes Zooming, blurring , sharpening , gray scale to color
conversion, detecting edges and vice versa , Image retrieval and
Image recognition. The common examples are:
Original Zoomed Blurr

36. EdgesSharp image

37. UV
imaging
•In the field of remote sensing , the area of the earth is scanned by a
satellite or from a very high ground and then it is analyzed to obtain
information about it. One particular application of digital image
processing in the field of remote sensing is to detect infrastructure
damages caused by an earthquake.

38. Hurdle
detection
•Hurdle detection is one of the common task that has been
done through image processing, by identifying different type
of objects in the image and then calculating the distance
between robot and hurdles.

39. Line
follower robot
•Most of the robots today work by following the line and thus are called
line follower robots. This help a robot to move on its path and perform
some tasks. This has also been achieved through image processing.

41. The continuum from image processing to
computer vision can be broken up into low-,
mid- and high-level processes
Low
Level Process
Input: Image
Output: Image
Examples: Noise
removal, image
sharpening
Mid
Level Process
Input: Image
Output: Attributes
Examples: Object
recognition,
segmentation
High
Level Process
Input:

Attributes Output:
Understanding
Examples:

Scene
understanding,
autonomous navigation
In this course we will stop here

42. 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
Outputs
of these processes generally are images
Knowledge Base

44. 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

45. 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.

47. 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.

49. Step
4: Colour Image Processing
Use the colour of the image to extract features of
interest in an image.
Colour modeling and processing in a digital domain etc.

50. Step
5: Wavelets
Are the foundation of representing images in various
degrees of resolution. It is used for image data
compression where images are subdivided into smaller
regions.

51. Step
6: Compression
Techniques for reducing the storage required to
save an image or the bandwidth required to
transmit it.

53. 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.
Step
7: Morphological Processing

55. 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.

57. 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.
Transforming raw data into a form suitable for subsequent
computer processing. Description deals with extracting
attributes that result in some quantitative information of
interest or are basic for differentiating one class of
objects from another.

60. Step
10: Object Recognition
Recognition: the process that assigns label to an object
based on the information provided by its description.
Recognition is the process that assigns a label, such as,
“vehicle” to an object based on its descriptors.

63. 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

64. 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.

65. 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.

66. 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.

67. 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

68. 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.

69. 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.

70. 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.

71. 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.