image processing

1. Digital Image Processing
Chapter 2: Digital Image
Fundamentals

2. Elements of Visual Perception
 Structure
of the
human
eye

3.  Rods and cones in the retina

4.  Image formation in the eye

5.  Brightness adaptation and
discrimination

6.  Brightness discrimination

7.  Weber ratio

8.  Perceived
brightness

9.  Simultaneous contrast

10.  Optical
illusion

11. Light and the Electromagnetic
Spectrum

12.  Wavelength


c


h
E 

13. Image Sensing and Acquisition

14.  Image acquisition using a single
sensor

15.  Using
sensor
strips

16.  A simple
image
formation
model

17.  Illumination and reflectance
 Illumination and transmissivity
)
,
(
)
,
(
)
,
( y
x
r
y
x
i
y
x
f 

18. Image Sampling and Quantization

19.  Sampling
and
quantization

20.  Representing digital images

22.  Saturation and noise

23.  Number of storage bits

24.  Spatial and gray-level resolution

25.  Subsampled
and
resampled

26.  Reducing
spatial
resolution

27.  Varying
the
number
of gray
levels

28.  Varying
the
number
of gray
levels

29.  N and k in different-details images

30.  Isopreference

31.  Interpolations

32.  Zooming
and
shrinking

33. Some Basic Relationships Between
Pixels
 Neighbors of a pixel
 : 4-neighbors of p
, , ,
: four diagonal neighbors of p
, , ,
: 8-neighbors of p
and
)
(
4 p
N
)
,
1
( y
x  )
1
,
( 
y
x
)
,
1
( y
x  )
1
,
( 
y
x
)
1
,
1
( 
 y
x )
1
,
1
( 
 y
x
)
1
,
1
( 
 y
x
)
1
,
1
( 
 y
x
)
( p
ND
)
(
8 p
N
)
(
4 p
N )
( p
ND

34.  Adjacency
 : The set of gray-level values used
to define adjacency
 4-adjacency: Two pixels p and q with
values from V are 4-adjacency if q is in
the set
 8-adjacency: Two pixels p and q with
values from V are 8-adjacency if q is in
the set
V
)
(
4 p
N
)
(
8 p
N

35.  m-adjacency (mixed adjacency): Two
pixels p and q with values from V are
m-adjacency if
 q is in , or
 q is in and the set
has no pixels whose values are from V
)
(
4 p
N
)
( p
ND
)
(
)
( 4
4 q
N
p
N 

37.  Subset adjacency
 S1 and S2 are adjacent if some pixel in
S1 is adjacent to some pixel in S2
 Path
 A path from p with coordinates to
pixel q with coordinates is a
sequence of distinct pixels with
coordinates
 , ,…,
where = , = ,
and pixels and are
adjacent
)
,
( y
x
)
,
( t
s
)
,
( 0
0 y
x )
,
( 1
1 y
x )
,
( n
n y
x
)
,
( 0
0 y
x )
,
( y
x )
,
( n
n y
x )
,
( t
s
)
,
( i
i y
x )
,
( 1
1 
 i
i y
x

38.  Region
 We call R a region of the image if R is a
connected set
 Boundary
 The boundary of a region R is the set of
pixels in the region that have one or
more neighbors that are not in R
 Edge
 Pixels with derivative values that
exceed a preset threshold

39.  Distance measures
 Euclidean distance
 City-block distance
 Chessboard distance
2
1
2
2
]
)
(
)
[(
)
,
( t
y
s
x
q
p
De 



|
)
(
|
|
)
(
|
)
,
(
4 t
y
s
x
q
p
D 



|)
)
(
|
|,
)
(
max(|
)
,
(
8 t
y
s
x
q
p
D 



40. m
D
 distance: The shortest m-path
between the points

41. An Introduction to the Mathematical
Tools Used in Digital Image Processing
 Linear operation
 H is said to be a linear operator if, for
any two images f and g and any two
scalars a and b,
)
(
)
(
)
( g
bH
f
aH
bg
af
H 



42.  Arithmetic
operations
 Addition

43.  Arithmetic
operations
 Subtraction

44.  Digital
subtraction
angiography

45.  Shading correction

46.  Image multiplication

47.  Set operations

48.  Complements

49.  Logical
operations

50.  Single-
pixel
operations

51.  Neighborhood
operations

52.  Affine transformations

53.  Inverse mapping

54.  Registration

55.  Vector
operations

56.  Image transforms

57.  Fourier transform

58.  Probabilistic methods