Presentation on theme: "Fundamentals of Digital Image"— Presentation transcript
Slide1
Fundamentals of Digital Image Processing & Machine Vision
By:
Dr. Rajeev Srivastava
Associate Professor, CSE, IIT(BHU), VaranasiSlide2
Fundamentals of Digital Image Processing
• Applications of image processing
• What's an image?
• A simple image model
• Fundamental steps in image processing
• Elements of digital image processing systemsSlide3
• Interest in digital image processing methods
stems from two
principal application areas:
(1)
Improvement
of pictorial information for
human interpretation, and
(2)
Processing
of scene data for autonomous
machine perception.Slide4Slide5
What's an image?
• A digital image is an image f(
x,y
) that has been
discretized both in spatial coordinates and
brightness.
• The elements of such a digital array are called
image elements or pixels.Slide6
A simple model
The scene is illuminated by a single source.
The
scene reflects radiation towards the camera.
The camera senses it via chemicals on film.Slide7
A simple image model:
•
To be suitable for computer processing, an image
f(
x,y
)
must be
digitized
both spatially and in
amplitude
.Slide8Slide9Slide10
The first thing we have to do, is to obtain signal values from the continuous signal at regular time-intervals.
This
process is known as sampling. The sampling interval is denoted as
and
its reciprocal, the
sampling frequency or
sample-rate is denoted as
,
where
= 1/
.
Slide11
Having defined our sampling interval
,
sampling
just extracts
the signals value at all integer multiples of
Ts
such that our discrete time sequence becomes:
x[n
] = x(n ·
Ts
)
Slide12
At
this point (after sampling), our signal is not yet completely digital because the values x[n]
can still take on any number from a continuous range - that’s why we use the terms discrete-time
signal here
and not digital signal.
Sampling a continuous sinusoidalSlide13Slide14
QuantizationSlide15Slide16Slide17
Now from our continuous range we assign that quantization level which is closest to our actual amplitude: the range 0.05...0.15 maps to 0.1 and so on.
That mapping can be viewed as a piecewise constant function acting on our continuous amplitude variable x.
Characteristic line of a
quantizer
- inputs from a continuous range x are mapped to
discrete levels
Slide18
Quantization NoiseSlide19
Basic Relationships BetweenPixelsSlide20
Neighborhood-
Slide21
Neighborhood-
The four diagonal neighbors of p
(
x,y
)
are given
by,
{(
x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1 ,y-1
)}
This set is denoted by
.
Each
of them are at Euclidean distance
of 1.414
from P.
Slide22
Neighborhood-
Slide23
Neighbors of a pixel
4-neighbors
of a pixel
p are
its
vertical and horizontal
neighbors denoted
by
8-neighbors
of a pixel
p are
its vertical horizontal
and
4 diagonal neighbors
denoted
by
Slide24Slide25
Adjacent pixelsSlide26
4-adjacency
:
Two pixels
p
and
q
with values from V are
4- adjacent
if q is in the set
.
8-adjacency
:
Two pixels
p
and
q
with values from V are
8- adjacent
if q is in the set
.
m-adjacency:
Two pixels
p
and
q
with values from V are
m-adjacent if
,
– q is in
.
– q is in
and the set [
]
is
empty (has
no pixels whose values are from V).
Slide27
Connectivity
It is used to
determine whether the
pixels are
adjacent in some sense.
Let V be the set of
gray-level values
used to define
connectivity; then
Two pixels p, q that have
values from
the set V are
:Slide28
4-connected,
if q is in the set
8-connected,
if q is in the set
m-connected,
iff
i. q is in
or
ii. q is in
and the set [
] is empty
Slide29
A
B
CSlide30
Adjacency/ConnectivitySlide31
Fundamental steps in image processing:
1
.
Image acquisition
: to
acquire
a digital image
2.
Image preprocessing:
to improve the image in
ways that increase the chances for success of the
other processes.
3.
Image segmentation:
to partitions an input image
into its constituent parts or objects.
4.
Image representation:
to convert the input data to
a form suitable for computer processing
.Slide32
Image Representations
Black and white image
single color plane with 2 bits
Grey scale image
single color plane with 8 bits
Color image
three color planes each with 8 bits
RGB, CMY, YIQ, etc.
Indexed color image
single plane that indexes a color table
Compressed images
TIFF, JPEG, BMP, etc.
2gray levels
4 gray levelsSlide33
Digital Image Representation (3 Bit Quantization)Slide34
Color QuantizationExample of 24 bit RGB Image
24-bit Color MonitorSlide35
Image Representation Example
128
135
166
138
190
132
129
255
105
189
167
190
229
213
134
111
138
187
135
190
255
167
213
138
128
138
129
189
229
111
166
132
105
190
134
187
24 bit RGB Representation (uncompressed)
Color PlanesSlide36
Graphical Representation
