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Engineering education
for today’s classroom
Outline
• How Can We Use Digital Images?
• A Digital Image is a Matrix
• Manipulating Images
• Improving Images for Robot Eyes
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Engineering education
for today’s classroom
4.1 How Can We Use Digital
Images?
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Engineering education
for today’s classroom
The Power of Digital Images
• It is easier and easier to acquire or
make digital images
 Digital
cameras and scanners
• Chief advantage over analog (e.g.
film) images: Easy to change
• How might we change images?
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Engineering education
for today’s classroom
Image Softening and Blurring
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Engineering education
for today’s classroom
Image Sharpening
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Engineering education
for today’s classroom
Finding Edges in Images
• Edges in images
•
usually define solid
objects in our 3D
world
Finding edges is a
first step to navigation
and manipulation
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Engineering education
for today’s classroom
Subtracting Images
•Image subtraction is a simple way to find
differences between two images
•Useful for security, recognition, and
navigation applications
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Engineering education
for today’s classroom
Image Segmentation
•
•
Segmentation:
Dividing images into
regions
Applications


Identifying
foreground vs.
background
Finding objects using
only one image
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Engineering education
for today’s classroom
4.2 A Digital Image Is a Matrix
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Engineering education
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Digital Images are Matrices
• Matrix:
A
=
An array of numbers
14 12 5
7
0
1
6
5
4
3
11 10
3 rows
and 4
columns
• Elements: A(i,j) , i and j are integers
Examples: A(2,3) = 6,
A(1,4) = 7, A(3,4) = 10
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Engineering education
for today’s classroom
Classes of Image Manipulation
1.
2.
Mapping: Pixel-by-pixel operations
Arithmetic Combination of Two Images:
e.g. addition, multiplication
3. Filtering: Operations on groups of pixels
in an image (neighborhood operation)
Fact: Almost all applications are made up of
simple combinations of these
manipulations
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Engineering education
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Image Mapping
• All of the form
B(i,j) = {function of A(i,j)}
where B(i,j) is the output image
• Many familiar effects:
 Brightness,
Contrast, Negative,
Thresholding, Gamma Correction
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Engineering education
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Brightness Mapping
k = 50
k = -50
B(i,j) = A(i,j) + k
k = constant
(expressed as a table)
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Engineering education
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Example: Brightness Mapping
Original
24-bit
image
Each color
increased
by 60
Each color
decreased
by 60
Green and
blue +60,
red -60
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Engineering education
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Contrast Mapping
• Formula:
B(i,j) = s • A(i,j)
If s > 1, contrast is increased
 If s < 1, contrast is decreased

• Need to quantize after multiplying, as
•
s • A(i,j) may not be an integer
Aside: This setting is found on most TV
sets
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Engineering education
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Contrast Mapping (cont.)
s>1
s<1
s > 1 and k < 0
s < 1 and k > 0
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Engineering education
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Example: Contrast and
Brightness
Higher
contrast
with
lower
brightness
Higher
contrast
• Formula: B(i,j) = s • A(i,j) + k
• This is easy to do!
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Engineering education
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Example: Contrast and
Brightness
• Given the 4-bit grayscale image
A=
14 12 5
7
0
1
6
5
4
3
11 10
• Compute the image with a
contrast of 1.5 and a
brightness of 2
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Engineering education
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Solution: Contrast and
Brightness
• Answer:
B(i,j) = 1.5•A(i,j) + 2
15 15 10 13
B=
2
4
11 10
8
7 15 15
Quantization causes
values such as 9.5 and
3.5 to be rounded up
Image is
clipped at 15
(Why?)
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Engineering education
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Infinity Project Experiment – 4.1
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Engineering education
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Brightness and Contrast Grayscale
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Engineering education
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Brightness and Contrast Color Image
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Engineering education
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Brightness and Contrast Color
Camera
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Engineering education
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Brightness and Contrast Cascade
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