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TRANSCRIPT
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Digital Image Processing
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Digital Image Processing
Digital Image Fundamentals
S. G. Lakhdive,
Head, Dept. of Computer Science,Prof. Ramkrishna More College, Akurdi, Pune
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Digital Image Processing
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Structure of the human eye
Image formation in the human eye
Brightness adaptation and discrimination
Outline
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Digital Image Processing
Human and Computer Vision
We cant think of image processing withoutconsidering the human vision system. We observeand evaluate the images that we process with our
visual system.
Without taking this elementary fact into consideration,we may be much misled in the interpretation ofimages.
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Structure of the human eye
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The cornea and sclera outer cover
The choroid
Ciliary bodyIris
Lens
The retina (two kinds of receptors)
Cones vision (photopic/bright-light vision) : centered at fovea, highlysensitive to color
Rods (scotopic/dim-light vision) : general view
Blind spot
Structure of the human eye
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Digital Image Processing
Lens & Retina
Lens : both infrared and ultraviolet light are absorbedappreciably by proteins within the lens structure and,in excessive amounts, can cause damage to the eye.
Retina : Innermost membrane of the eye which linesinside of the walls entire posterior portion. When theeye is properly focused, light from an object outside
the eye is imaged on the retina.
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Receptors
Pattern vision is afforded by the distribution of discrete light
receptors over the surface of the retina.
Receptors are divided into 2 classes:
Cones
Rods
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Cones
6-7 million, located primarily in the central portion ofthe retina (the fovea, muscles controlling the eyerotate the eyeball until the image falls on the fovea)
Highly sensitive to color.
Each is connected to its own nerve end thus humancan resolve fine details.
Cone vision is called photopic or bright-light vision.
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Digital Image Processing
Rods
75-150 million, distributed over the retina surface.
Several rods are connected to a single nerve end
reduce the amount of detail discernible.Serve to give a general, overall picture of the field ofview.
Sensitive to low levels of illumination.
Rod vision is called scotopic or dim-light vision.
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Cross Section Right eye
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Image formation in the human eye
LightReceptor
BrainRadiant
Energy
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Dynamic range of human visual system: 10-6~104 mL
(millilambert)
Can not accomplish this range simultaneously
The current sensitivity level of the visual system is called
brightness adaptation level
Brightness adaptation
i i l i
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Brightness adaptation
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Di it l I P i
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Weber ratio (the experiment) :
I: the background illumination
: the increment of illumination
Small Weber ratio indicates good discrimination
Larger Weber ratio indicates poor discrimination
Weber's Law states that the ratio of the increment
threshold to the background intensity is a constant.
So when you are in a noisy environment you must
shout to be heard while a whisper works in a quiet
room. And when you measure increment thresholds
on various intensity backgrounds, the thresholds
increase in proportion to the background.
Brightness discrimination
IIC
/
CI
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Brightness discrimination
Di it l I P i
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The perceived brightness is not a simple function of
intensity
Mach band pattern
Simultaneous contrast
Optical illusion
Psycho-visual effects
Di it l I P i
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Mach band pattern
Di it l I P i
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Simultaneous contrast
Digital Image Processing
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Optical illusion
Digital Image Processing
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Optical illusion
Digital Image Processing
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Optical illusion
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Digital Image Processing
Sampling, Quantization and Other SimpleOperations
Digital Image Processing
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Sampling, Quantization, and Operations
Digital image representation
Image formation model
Uniform sampling
Uniform quantization
Relationships between pixels
Outline
Digital Image Processing
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Digital Image Representation
A digital image is an imagef(x,y) that has been digitized
both in spatial coordinates and brightness.
The value of f at any point (x,y) is proportional to thebrightness (or gray level) of the image at that point.
A digital image can be considered a matrix whoserow and column indices identify a point in the image
and the corresponding matrix element value identifiesthe gray level at that point.
Digital Image Processing
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Digital Image Representation
Pixel values in highlighted region
CAMERA => DIGITIZER =>A set of number in 2D grid
Samples the analog data and digitizes it.
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Sampling, Quantization and Other SimpleOperations
0 ,f x y
,f x y
,i x y
may be characterized by two components :
Illumination: Reflectance: ,r x y
, , ,f x y i x y r x y
0 ,i x y 0 , 1r x y
Monochrome image
Typical values of the illumination and reflectance:Illumination: sun on earth: 90,000 lm/m2 on a sunny day; 10,000 lm/m2 on
a cloud day; moon on clear evening: 0.1 lm/m
2
; in a commercial office isabout 1000 lm/m2
Reflectance: 0.01 for black velvet, 0.65 for stainless steel, 0.80 for flat-white
wall paint, 0.90 for silver-plated metal, and 0.93 for snow
Image Formation Model
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Sampling, Quantization, and Operations
Sampling
digitalized in
spatial domain
Quantizationdigitalized in
amplitude
Uniform sampling and quantization
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Sampling, Quantization, and Operations
Uniform sampling and quantization
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Sampling, Quantization, and Operations
Spatial resolution : It is a measure of smallest discernible
details in an image. The more pixels in a fixed range, the
higher the resolution
Gray-level resolution : the more bits, the higher the
resolution
Image resolution
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g g g
Neighbors of a pixel
Neighbors of pixel are the pixels that are adjacent
pixels of an identified pixel.
Each pixel is a unit distance from the particularpixel.
Some of the neighbors of pixel lie outside the
digital image if its position is on the border of the
image.
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g g g
Pixel at coordinate (column x, rowy) can be represented by f(x,y)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0123
456789
101112131415
Pixel at the
7th
columnand 4th rowis yellow color
Zoom 1600%
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g g g
4-neighbors of pixel
4-neighbors of pixel is denoted by N4(p)
It is set of horizontal and vertical neighbors
f(x,y)is a yellow circlef(x,y-1)is top onef(x-1,y)is left one
f(x+1,y)is right onef(x,y+1)is bottom one
(x-1)
(y-1)
(y+1)
(x+1)(x)
(y)
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Diagonal neighbors of pixel
Diagonal neighbors of pixel is denoted by ND(p)
It is set of diagonal neighbors
f(x,y)is a yellow circlef(x-1,y-1)is top-left onef(x+1,y-1)is top-right onef(x-1,y+1)is bottom-left one
f(x+1,y+1)is bottom-right one
(x-1)
(y-1)
(y+1)
(x+1)(x)
(y)
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8-neighbors of pixel
8-neighbors of pixel is denoted by N8(p)
4-neighbors and Diagonal neighbors of pixel
f(x,y)is a yellow circle(x-1,y-1), (x,y-1),(x+1,y-1),(x-1,y), (x,y), (x+1,y),
(x-1,y+1),(x,y+1), (x+1,y+1)
(x-1)
(y-1)
(y+1)
(x+1)(x)
(y)
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Connectivity
Establishing boundaries of objects and components of
regions in an image.
Group the same region by assumption that the pixels beingthe same color or equal intensity will are the same region
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Connectivity
Let C is the set of colors used to define
There are three type of connectivity:
4-Connectivity : 2 pixels (p and q) with value in C are 4-connectivity if
q is in the set N4(p)
8-Connectivity : 2 pixels (p and q) with value in C are 8-connectivity if
q is in the set N8(p)
M-Connectivity : 2 pixels (p and q) with value in C are 8-connectivity
if(i) Q is in N4(p), or
(ii) Q is in ND(p) and the set N4(p) N4(q) has no pixels whose value are from
V
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Sampling, Quantization, and Operations
m-adjacency
Relationships between pixels
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Distance Measure
For pixels p, q, and z, with coordinates (x,y), (s,t) and
(u,v) respectively, D is a distance function or metric if
(a) D(p,q) 0 and
(b) D(p,q) = 0 iff p = q and
(c) D(p,q) = D(q,p) and
(d) D(p,z) D(p,q) + D(q,z)
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The D8 Distance
Also called chessboard distance
Calculate between p and q is defined as
D8(p,q) = max(| x - s |,| y - t |)
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