-
Digital Image Processing and Edge Detection
Digital Image
Processing
Interest
in
digital
image
processing
methods
stems
from
two
principal
applica-
tion
areas:
improvement
of
pictorial
information
for
human
interpretation;
and
processing
of
image
data
for
storage,
transmission,
and
representation
for au- tonomous
machine
perception.
An
image
may
be
defined
as
a
two-dimensional
function,
f(x,
y),
where x and y are
spatial
(plane)
coordinates,
and
the amplitude
of
f
at
any pair of
coordinates (x,
y)
is called
the
intensity
or
gray level
of the image
at that point. When x, y, and the
amplitude
values of
f
are all finite, discrete
quantities,
we
call
the
image
a
digital
image
.
The
field
of
digital
image
processing
refers to processing digital images by
means of a digital computer.
Note
that
a
digital
image
is composed
of a finite
number
of elements,
each
of which has a
particular
location
and value. These
elements
are
referred
to
as
picture
elements
,
image
elements
,
pels
, and
pixels
.
Pixel
is the
term
most
widely used
to denote
the
elements
of a digital image.
Vision is the most advanced
of our senses, so it is not surprising
that
images
play
the
single
most
important
role
in
human
perception.
However,
unlike
humans,
who
are
limited
to
the
visual
band
of
the
electromagnetic
(EM)
spec-
trum,
imaging
machines
cover
almost
the entire
EM
spectrum,
ranging
from gamma to radio waves.
They can
operate
on
images
generated
by
sources
that
humans
are
not
accustomed
to
associating
with
images.
These
include
ultra-
sound,
electron
microscopy,
and
computer-generated
images.
Thus,
digital
image processing
encompasses
a
wide and
varied
field of
applications.
There
is no general
agreement
among
authors
regarding
where
image
processing stops
and
other
related
areas,
such as image
analysis
and
computer
vi-
sion,
start.
Sometimes
a
distinction
is
made
by
defining image processing
as a discipline in which both the input
and output
of
a
process
are
images.
We
believe
this
to
be
a
limiting
and
somewhat
artificial
boundary.
For
example,
under
this
definition,
even
the
trivial
task
of
computing
the
average
intensity
of
an
image
(which
yields
a
single
number)
would
not
be
considered
an
image
processing
operation.
On
the
other
hand,
there
are
fields
such
as
computer
vision
whose
ultimate
goal is to use computers
to emulate
human
vision, including
learning
and
being
able
to
make
inferences
and
take
actions
based
on
visual
inputs.
This
area
itself
is
a
branch
of
artificial
intelligence
(AI)
whose
objective
is
to
emulate
human
intelligence.
The
field
of AI
is in
its
earliest
stages
of infancy
in
terms of
development,
with
progress
having
been
much
slower
than
originally
anticipated.
The
area
of image
analysis
(also called image understanding)
is in be- tween
image processing
and
computer
vision.
There
are
no
clearcut
boundaries
in
the
continuum
from
image
processing
at
one
end
to
computer
vision
at
the
other.
However,
one
useful
paradigm
is
to
consider
three
types
of
computerized
processes
in
this
continuum:
low-,
mid-,
and
highlevel
processes.
Low-level
processes
involve
primitive
opera-
tions
such
as
image
preprocessing
to
reduce
noise,
contrast
enhancement,
and
image
sharpening.
A
low-level
process
is
characterized
by
the
fact
that
both
its
inputs
and
outputs
are
images.
Mid-
level
processing
on
images
involves tasks
such
as
segmentation
(partitioning
an
image
into regions
or
objects), description
of
those
objects
to reduce
them
to
a
form
suitable
for
computer
processing,
and
classification
(recognition)
of
individual
objects.
A
midlevel
process
is
characterized
by
the
fact
that
its
inputs
generally
are
images,
but
its
outputs
are
attributes
extracted
from
those
images
(e.g., edges, contours,
and the
identity
of
individual
objects).
Finally,
higherlevel
processing
involves
“makin
g
sense
”
of an
ensemble
of recognized
objects,
as in
image
analysis,
and,
at
the
far
end
of
the
continuum,
performing
the
cognitive
functions
normally associated
with vision.
Based
on
the
preceding
comments,
we
see
that
a
logical
place
of
overlap
between
image
processing
and
image
analysis
is
the
area
of
recognition
of
individual
regions
or
objects
in an
image. Thus,
what
we
call
in
this
book
digital
image
processing
encompasses
processes
whose
inputs
and
outputs
are
images
and,
in
addition,
encompasses
processes
that
extract
attributes
from
images,
up
to
and
including
the
recognition
of
individual
objects.
As
a
simple
illustration to
clarify
these
concepts,
consider
the
area
of
automated
analysis
of
text.
The
processes
of
acquiring
an
image
of
the
area
containing
the
text,
preprocessing
that
image,
extracting
(segmenting)
the
individual
characters,
describing
the
characters
in
a
form
suitable
for
computer
processing,
and
recognizing
those
individual
characters
are
in
the
scope of
what we call digital image processing in this
book. Making
sense of
the
content
of the
page
may be viewed
as being in the
domain
of image
analysis and even computer
vision, depending
on the level of complexity
implied
by the
statement
“making
sense.
”
As will
become evident
shortly,
digital
image
processing,
as
we
have
defined
it, is used
successfully
in
a
broad
range
of areas
of
exceptional
social and
economic
value.
The areas of
application
of digital
image processing
are so
varied that
some form
of
organization
is
desirable
in
attempting
to
capture
the
breadth
of
this
field.
One
of
the
simplest
ways
to
develop
a
basic
understanding
of
the
extent
of
image
processing
applications
is
to
categorize
images according
to their source (e.g., visual, X-ray,
and so on).
The
principal
energy
source
for
images
in
use
today
is
the
electromagnetic
energy
spectrum.
Other
important
sources
of
energy
include
acoustic,
ultrasonic,
and electronic
(in the form of electron
beams
used in electron
microscopy).
Synthetic
images,
used
for
modeling
and
visualization,
are
generated
by computer.
In
this section
we discuss briefly
how images
are
generated
in
these
various
categories
and
the
areas
in
which
they
are
applied.
Images
based
on
radiation
from
the
EM
spectrum
are
the
most
familiar,
es- pecially
images
in the
X-ray
and
visual bands
of the
spectrum.
Electromagnet-
ic
waves
can
be
conceptualized
as
propagating
sinusoidal
waves
of varying wavelengths,
or they
can be thought
of as
a stream
of massless particles,
each traveling
in a wavelike pattern
and
moving
at
the
speed
of
light.
Each
massless
particle
contains
a
certain
amount
(or
bundle)
of energy. Each
bundle
of energy is called
a
photon
.
If spectral
bands
are
grouped
according
to
energy
per photon,
we
obtain
the
spectrum
shown
in
fig.
below,
ranging
from
gamma
rays
(highest
energy)
at
one
end
to
radio
waves
(lowest
energy)
at
the
other.
The bands
are
shown
shaded
to
convey
the
fact
that
bands
of the
EM
spectrum
are not
distinct
but
rather
transition
smoothly
from
one
to the
other.
Image
acquisition
is
the
first
process. Note that
acquisition
could
be
as
simple
as being
given an
image
that
is already
in digital
form.
Generally,
the
image
acquisition
stage
involves
preprocessing,
such as scaling.
Image
enhancement
is
among
the
simplest
and
most
appealing
areas
of digital image
processing.
Basically, the
idea
behind
enhancement
techniques
is to
bring out detail that is obscured, or simply to
highlight certain
features
of interest in
an
image.
A
familiar
example
of
enhancement
is
when we increase
the contrast
of
an image because
“it
looks
better.
”
It
is
important
to
keep
in mind
that enhancement
is a very subjective
area
of
image
processing.
Image
restoration
is
an
area
that
also
deals
with
improving
the
appearance
of
an
image.
However,
unlike
enhancement,
which
is
subjective,
image
restoration
is
objective,
in
the
sense
that
restoration
techniques
tend
to
be
based
on
mathematical
or
probabilistic
models of image degradation.
Enhancement,
on the other
hand,
is
based
on
human
subjective
preferences
regarding
what
constitutes
a
“good”
enhancement
result.
Color
image
processing
is
an
area
that
has
been
gaining
in
importance
because
of
the
significant
increase
in
the
use
of
digital
images over the Internet. It covers a
number
of fundamental
concepts
in
color
models
and
basic color
processing
in a
digital
domain.
Color
is used
also in later
chapters
as the
basis for extracting
features
of
interest
in
an
image.
Wavelets
are
the
foundation
for
representing
images
in
various
degrees
of
resolution.
In
particular,
this
material
is
used
in
this
book
for
image
data
compression
and
for
pyramidal
representation,
in
which
images
are subdivided successively into
smaller
regions.
-
-
-
-
-
-
-
-
-
上一篇：(完整版)大学英语四级常考词组固定搭配
下一篇：外研版英语必修四短语复习