Digital Image Processing - Introduction - History - Basics
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Digital Image Processing
Introduction
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ARVIND PANDE
Visit-arvindpandeblog.blogspot.in
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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
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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
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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
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Computerized Processes Types
High-Level Processes:
Image analysis and computer vision
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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.
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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
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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
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1 pixel
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What is a Digital Image? 10
Common image formats include:
1 sample per point (B&W or Grayscale)
2 samples per point (Red, Green, and Blue)
3 samples per point (Red, Green, Blue, and “Alpha”, a.k.a.
Opacity)
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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
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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
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Early digital image
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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
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Improved
digital image Early 15 tone digital
image
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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
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A picture of the moon taken
by the Ranger 7 probe
minutes before landing
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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
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Typical head slice CAT
image
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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
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Examples: Image Enhancement
One of the most common uses of DIP techniques: improve
quality, remove noise etc
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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
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Examples: Artistic Effects
Artistic effects are used to make
images more visually appealing, to
add special effects and to make
composite images
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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
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Original MRI Image of a Dog Heart Edge Detection Image
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Examples: GIS
Geographic Information Systems
Digital image processing techniques are used extensively to
manipulate satellite imagery
Terrain classification
Meteorology
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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
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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?
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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
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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
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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
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Key Stages in Digital Image
Processing
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Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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Key Stages in Digital Image
Processing:
Image Aquisition
29
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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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
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Key Stages in Digital Image
Processing:
Image Enhancement
31
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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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.
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Key Stages in Digital Image
Processing:
Image Restoration
33
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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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.
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Key Stages in Digital Image Processing:
Morphological Processing35
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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Fundamental Steps in DIP:
(Description)
Step 4: 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.
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Key Stages in Digital Image
Processing:
Segmentation
37
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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Fundamental Steps in DIP:
(Description)
Step 5: 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.
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Key Stages in Digital Image
Processing:
Object Recognition
39
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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Fundamental Steps in DIP:
(Description)
Step 6: Recognition and Interpretation
Recognition: the process that assigns label to an object based on the information provided by its
description.
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Key Stages in Digital Image Processing:
Representation & Description41
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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Fundamental Steps in DIP:
(Description) Step 7: 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 (انحناءات)
- Region Representation: Focus on internal properties, such as texture or skeleton shape (هيكلية)
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Key Stages in Digital Image
Processing:
Image Compression
43
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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Fundamental Steps in DIP:
(Description)
Compression
Techniques for reducing the storage required to save an image or the bandwidth required to
transmit it.
Wavelets
Are the foundation of representing images in
various degrees of resolution. It is used for image
data compression.
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Key Stages in Digital Image
Processing:
Colour Image Processing
45
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
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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.
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Components of Image Processing System
.
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Components of the System
Image Sensor:
With reference to sensing, two elements are required to acquire digital images:
a sensor and a digitizer.
The sensor that is sensitive to the energy radiated by the object we wish to image. The second, called a digitizer, is a device for converting the output of the physical sensing device into digital form.
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Components of the System
For example, in a digital video camera, the sensors produce an electrical output proportional to light intensity. The digitizer converts these outputs to digital data.
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Components of the System
Specialized image processing hardware:
usually consists of the digitizer just mentioned, plus hardware that performs other primitive operations, such as an arithmetic logic unit (ALU).
ALU performs arithmetic and logical operations in parallel on entire images. ALU is used is in averaging images as quickly as they are digitized, for the purpose of noise reduction.
This type of hardware sometimes is called a front-end subsystem, and its most distinguishing characteristic is speed in which they process image
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Components of the System
Computer:
in an image processing system is a general-purpose computer and can range from a PC to a supercomputer.
In dedicated applications, some times specially designed computers are used to achieve a required level of performance.
Software:
for image processing consists of specialized modules that perform specific tasks
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Components of the System
Mass storage:
capability is a must in image processing applications.
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, and
(3) archival storage, characterized by infrequent access
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Image display: it displays images.
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.
Components of the System53
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