0 Image Processing & Remote Sernsing.pdf

Video Processing/
Digital Image Processing
Dr. A. K. Bhandari
Ph.D (Satellite Image Processing)
17 July 2019

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Introduction
“One picture is worth more than ten
thousand words”
Anonymous

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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
(Image Enhancement & Segmentation)
(Satellite Image Processing)

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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
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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What is a Digital Image? (cont…)
Common image formats include:
– 1 sample per point (Grayscale)
– 3 samples per point (Red, Green, and Blue)
For most of this course we will focus on grey-scale
images

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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
Early digital image
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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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
Improved
digital image Early 15 tone digital
image
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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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
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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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
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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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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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
Typical applications:
 Noise filtering
 Content Enhancement
 Contrast enhancement
 Deblurring
 Remote sensing

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Key Stages in Digital Image Processing:
Image Acquisition
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)

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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)
Two types of Work Area:
I) Special Domain
II)Frequency Domain

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Basic Gray Level Transformations
• Image Negatives
• Log Transformations
• Power Law Transformations
• Piecewise-Linear Transformation
Functions
• For the following slides L denotes the max. possible gray value of
the image, i.e. f(x,y)  [0,L]

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Image Negatives
Input gray level
Output
gray
level
T(f)=L-f
Image Negatives: T(f)= L-f

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Log Transformations
T(f) = c * log (1+ f)
This transformation maps a narrow range of low gray-level values into a wider range of output
levels.
The opposite is true of higher values of input levels.
During log transformation , the dark pixels in an image are expanded as compare to the higher
pixel values. The higher pixel values are kind of compressed in log transformation.
InvLog Log

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Power Law Transformations
(Gamma Correction)
T(f) = c*f 

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Used for gamma-correction

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Used for general purpose contrast
manipulation

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Image Enhancement
(Gamma Correction)

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Piecewise Linear Transformations
Contrast Stretching

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Thresholding Function
• g(x,y) = L if f(x,y) > t,
• 0 else
• t = ‘threshold level’
Input gray level
Output
gray
level

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Image Histograms
Histogram of an image represents the relative
frequency of occurrence of various gray levels in
the image
0 50 100 150 200
0
500
1000
1500
2000
2500
3000
MATLAB function >imhist(x)

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Why Histogram?
0 50 100 150 200 250
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
It is a baby in the cradle!
Histogram information
reveals that image is
under-exposed
0 50 100 150 200 250
0
1000
2000
3000
4000
5000
6000
7000
Over-exposed image

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Histogram Equalization

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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)

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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)

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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)

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Satellite Image Segmentation
Image
Acquisition
Image
Restoration
Morphological
Processing
Representation &
Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Segmentation
Fig. 7: Key Stages Used in Digital Image Processing
Satellite Image
Thresholded Image
into k levels
Segmented Image
Easy analysis
Simple Interpretation
Exposed features
Compressed data
Consume less memory

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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)

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36
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)

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36
Image Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

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Colour Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

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Image Segmentation & it’s uses
 The objective of image segmentation is to extract meaningful objects. A
meaningful segmentation selects the proper threshold values to optimize a criterion
using entropy.
 Bi-level thresholding & Multilevel thresholding
 Segmentation should stop when the objects of interest in an application have been
isolated.
Application
 After a successful segmentation of the image, the contours of objects can be
extracted using edge detection and/or border.
 Shape of objects can be described.
 Based on shape, texture, and color objects can be identified.
 Image segmentation techniques are extensively used in similarity searches, e.g.

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Image Segmentation
 Segmentation algorithms are based on one of two basic properties of
intensity values discontinuity and similarity.
1. First category is to partition an image based on abrupt changes in
intensity, such as edges in an image.
2. Second category are based on partitioning an image into regions that are
similar according to a predefined criteria. Histogram Thresholding
approach falls under this category.

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Thresholding - Foundation
Basic Global & Local Thresholding:
 A point (x,y) belongs to
 to an object class if T1 < f(x,y)  T2
 to another object class if f(x,y) > T2
 to background if f(x,y)  T1
 T depends on
 only f(x,y) : only on gray-level values  Global threshold
 both f(x,y) and p(x,y) : on gray-level values and its neighbors  Local
threshold
Fig. 8: Gray-level histogram that can be partitioned by (a) A single threshold and (b) multilevel threshold
image with dark
background and
a light object
image with dark
background and
two light object

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 Basic Global Thresholding:
Fig. 9: Original image, (b) image histogram, (c) Result of global thresholding with T midway between the
maximum and minimum gray levels.
use T midway
between the max
and min gray levels

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 Basic Local Thresholding:
Fig. 10: Computer generated reflectance function, (b) Histogram of reflectance function, (c) Computer
generated illumination function, (d) Product of (a) and (c), (e) Histogram of product image.
f(x,y) = i(x,y) r(x,y)
(a). computer generated reflectance
function
(b). histogram of reflectance function
(c). computer generated illumination
function (poor)
(d). Product of (a) and (c).
(e). Histogram of product image
difficult to segment
easily use global thresholding
object and background are separated
 since the threshold used for each pixel
depends on the location of the pixel in
terms of the subimages, this type of
thresholding is adaptive.

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Examples: Image Enhancement
One of the most common uses of DIP
techniques: improve quality, remove noise
etc
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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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 Magnetic Resonance Imaging
(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
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Ultrasound imaging (Medical)
Fig. Fetus and Thyroid using ultrasound

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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?
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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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
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Examples: HCI
Try to make human computer
interfaces more natural
– Face recognition
– Gesture recognition
These tasks can be
extremely difficult

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RS as Source of Information

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Introduction of Remote Sensing
 (Swain and Davis, 1978), (Campbell, 1996)
 River, Growth of vegetation.
 Study about Residential, Industrial area, Roads and Building.
Fig. 1: Application of areal images in various fields.

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Introduction Cont…
Fig. 2: INSAT and LANDSAT band combination.

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Introduction Cont…
 Energy source: Passive/Active
 Atmosphere
 Target
 Recording devices
 Transmission/reception/processing
 Interpretation
 Application
Fig. 3: Flows of energy and information in remote sensing.

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Examples: GIS
Geographic Information Systems
– Digital image processing techniques are used
extensively to manipulate satellite imagery
– Terrain classification (Land)
– Meteorology (atmosphere, weather)
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Example of Remote Sensing
Fig. Satellite images of Mumbai suburban(Left) and Gateway of India (Right)

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UV imaging (Ozone)
Fig. Detect ozone layer damage

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Field of Application in Satellite Image Processing
 Meteorology- Weather, Climate, Global
 Hydrology- Water, Energy
 Soil Science- Land, Soil
 Biology/ Nature Conservation- Vegetation
 Forestry- Investigation, Fire
 Environmental Studies- Pollution, Climate
 Agricultural Engineering-Land Use Change
 Physical Planning
 Land Surveying
 Sea Ice
 Land Cover & Land Use

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Summary
We have looked at:
– 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
Next time we will start to see how it all
works…

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