Fundamentals of Computer Vision Lecture 4 Dr. Roger S. Gaborski Roger S. Gaborski

Transcription

Fundamentals of Computer Vision Lecture 4 Dr. Roger S. Gaborski Roger S. Gaborski
Fundamentals of Computer Vision
Lecture 4
Dr. Roger S. Gaborski
Roger S. Gaborski
1
QUIZ 1
Closed book except one page of
handwritten notes
Roger S. Gaborski
2
Intensity Transformation and
Spatial Transformation
• Intensity – modify each pixel in the image
independent of its neighbors
• Spatial – modify a pixel as a function of its
neighbors (spatial convolution)
Roger S. Gaborski
3
If you look at a pixel of an intensity
(gray level image- Next Slide) in
isolation what can you tell me about
the image?
Roger S. Gaborski
4
Roger S. Gaborski
5
If you look at a pixel of an intensity
(gray level image- Next Slide) in
isolation what can you tell me about
the image?
The brightness or intensity value at that spatial location
Roger S. Gaborski
6
What if you inspect a neighborhood
of pixel values?
Roger S. Gaborski
7
Depends on the neighborhood
Sky Region
Flag Region (2 rows, 3 columns)
The ‘flag region’ contains more information about the image
– There is a horizontal line in this region
Roger S. Gaborski
8
Can we modify the intensity values to
change the image (improve in some
way) or extract information from the
image (edges, shapes, etc.)?
Roger S. Gaborski
9
Spatial Transformation
• The general spatial domain processing equation:
g(x, y) = T [ f (x, y)]
– f (x, y) is the input image
– g(x, y) is the output image
– T is an operator on f defined over a specific neighborhood
Roger S. Gaborski
10
Chapter 3
www.prenhall.com/gonzalezwoodseddins
- Define spatial neighborhood about a point,
(x, y)
- Neighborhood is typically a square or
rectangle, but could be other shapes
- Center of neighborhood is moved from pixel
to pixel starting at the upper left hand corner
of the Image
- Operator T is applied at each location (x, y)
to yield g(x, y) at that location
Roger S. Gaborski
11
Intensity Transformations
• Let the neighborhood be reduced to a size of 1 x 1
pixel
• Now the transformation is only a function of the
pixel itself.
• This is commonly called an intensity transformation
or gray level transformation function:
s = T(r)
where r is the intensity of f at (x, y) and s is the resulting
intensity of g at (x, y)
Roger S. Gaborski
12
Intensity Transformations
• Assume the range of pixel intensity values is [0,1]
• What can be accomplished with transformation T ?
Roger S. Gaborski
13
Intensity Transformations
>> R = rand([1 4])
R=
0.0272 0.7937 0.9992 0.1102
>> R2 = R .* R ( note .* and not simply * )
R2 =
0.0007 0.6299 0.9985 0.0122
(small numbers are much smaller)
>> RSR = sqrt(R)
RSR =
0.1649 0.8909 0.9996 0.3320
Roger S. Gaborski
14
R = rand(1, 4)
R2 = R .* R
RSR = sqrt(R)
In this case the data is actually changed
Not the only display as in previous lecture
Roger S. Gaborski
15
Characteristics:
• y = x2 : all pixels become darker
• y = sqrt(x): all pixels become
brighter
• y = x: all pixels remain the same
value
Output (y)
Input (x)
Roger S. Gaborski
16
Intensity Transformations
• Function imadjust
– intensity transformation of gray scale images
• g = imadjust( f, [low_in high_in], [low_out,
high_out], gamma)
– Input - output defined in [0,1] range:
• [low_in high_in]  [0 1] (min max)
• [low_out high_out]  [0 1] (min max)
Roger S. Gaborski
17
Characteristics:
• gamma >1 : all pixels become darker
• gamma <1 : all pixels become brighter
• gamma =1 : linear transform
Gamma specifies the shape of the curve
Brighter Output (gamma<1)
Darker Output (gamma>1)
Chapter 3
www.prenhall.com/gonzalezwoodseddins
Roger S. Gaborski
18
>> I = imread('mammogram.tif');
>> figure, imshow(I)
Roger S. Gaborski
19
>> I2 = imadjust(I, [.5 .75], [0 1]);
>> figure, imshow(I2)
Roger S. Gaborski
20
Reduce the amount of tissue Mapped to black:
>> I3 = imadjust(I, [.25 .75], [0 1]);
>> figure, imshow(I3)
Roger S. Gaborski
21
.75
1.0
Output
.50
I3
.25
I2
.25
.50
.75
1.0
Roger S. Gaborski
Input
22
I4 = imadjust(I, [0, 1], [0, 1], 2)
Gamma=2
Roger S. Gaborski
23
Negative of image:
>> I4 = imadjust(I,[0 1], [1 0]);
>> figure, imshow(I4)
Or
I4 = imcomplement(I);
Roger S. Gaborski
24
Example, continued
H = [.5 .5 .6 .7 .8 .2; .6 .3 .8 .9 .2 .4 ]
H=
0.5000 0.5000 0.6000 0.7000 0.8000 0.2000
0.6000 0.3000 0.8000 0.9000 0.2000 0.4000
b = imadjust(H, [.3 .7],[.1 .9],1)
Roger S. Gaborski
25
1.0 OUTPUT
0
.1
2
.3
.4
.5
.6
.7
.8
.9
Example
0
.1
.2
.3
.4
Roger.5
S. Gaborski .6
.7
.8
.9
1.0
26
INPUT
1.0 OUTPUT
0
.1
2
.3
.4
.5
.6
.7
.8
.9
Example:b=imadjust(H, [.3 .7],[.1 .9],1)
0
.1
.2
.3
.4
Roger.5
S. Gaborski .6
.7
.8
.9
1.0
27
INPUT
1.0 OUTPUT
0
.1
2
.3
.4
.5
.6
.7
.8
.9
Example:b=imadjust(H, [.3 .7],[.1 .9],1)
0
.1
.2
.3
.4
Roger.5
S. Gaborski .6
.7
.8
.9
1.0
28
INPUT
Example, continued
H = [.5 .5 .6 .7 .8 .2; .6 .3 .8 .9 .2 .4 ]
H=
0.5000 0.5000 0.6000 0.7000 0.8000 0.2000
0.6000 0.3000 0.8000 0.9000 0.2000 0.4000
b=imadjust(H, [.3 .7],[.1 .9],1)
b=
0.5000 0.5000 0.7000 0.9000 0.9000 0.1000
0.7000 0.1000 0.9000 0.9000 0.1000 0.3000
Roger S. Gaborski
29
Logarithmic Transformation
• g = log(f ), where f is the input image
– Issue: log(0) = -Inf
– Any zeros in the input will be mapped to -Inf
– Note: log(1) = 0
• g = log(1+f )
– This ensures that log(f ) is never mapped to -Inf
– Similar to gamma transformation
– Commonly used for dynamic range compression
Roger S. Gaborski
30
Contrast Stretching Transformation
• Creates an image with higher contrast than the input
image:
1
s  T (r ) 
m
1  ( )E
r
– r: intensities of input;
– s: intensities of output;
– m: threshold point (see graph) ;
– E: controls slope.
Roger S. Gaborski
31
Chapter 3
www.prenhall.com/gonzalezwoodseddins
E controls the slope of the function
Roger S. Gaborski
32
>> I = imread('mammogram.tif');
>> figure, subplot(2,2,1), imshow(I), title('I')
>> E= 1;
>> g = 1./(1+(m./(double(I)+eps)).^E);
>> subplot(2,2,2), imshow(g),title('E=1')
>> E= 3;
>> g = 1./(1+(m./(double(I)+eps)).^E);
>> subplot(2,2,3), imshow(g),title('E=3')
>> E= 5;
>> g = 1./(1+(m./(double(I)+eps)).^E);
>> subplot(2,2,4), imshow(g),title('E=5')
Roger S. Gaborski
33
1
s  T (r ) 
m E
1 ( )
r
Output(s)
Input(r)
Roger S. Gaborski
34
Roger S. Gaborski
35
Chapter 3
www.prenhall.com/gonzalezwoodseddins
Input
Output
Roger S. Gaborski
36
Gray Image
>> I = imread('Flags.jpg');
>> figure, imshow(I) % uint8
>> Im = im2double(I); % convert to double
>> Igray = (Im(:,:,1)+Im(:,:,2)+Im(:,:,3))/3;
>> figure, imshow(Igray)
There is also the rgb2gray function that results
in a slightly different image
Roger S. Gaborski
37
rgb2gray
help rgb2gray
rgb2gray Convert RGB image or colormap to grayscale.
rgb2gray converts RGB images to grayscale by eliminating the
hue and saturation information while retaining the
luminance.
I = rgb2gray(RGB) converts the truecolor image RGB to the
grayscale intensity image I.
Roger S. Gaborski
38