CS 423 (CS 423/CS 523)
Computer Vision
Lecture 1
INTRODUCTION TO COMPUTER VISION
About the
Course
2
Syllabus
http://vvgl.ozyegin.edu.tr
Objective
Introduction to the theory, tools, and algorithms of computer vision
Instructor
Assist. Prof. M. Furkan Kıraç
E-mail: furkan.kirac@ozyegin.edu.tr
Room: 219
Hours
Mondays, 9:40-12:30, Room: 246
Grading
Projects: 4x15%
Midterm Exam: 40%
3
Grading

Projects:
Late submissions are not accepted. Copying
answers from others’ work is not permitted.

Midterm Exam:
At least 3 of the 4 Projects must be turned in by
the due date in order to qualify for the Final Exam.
No Composite Exam (Bütünleme Sınavı), as there
is no final exam.
4
Recommended Books

Computer Vision: Algorithms and Applications,
Richard Szeliski, Springer, 2010.

Computer Vision: A Modern Approach, David A.
Forsyth and Jean Ponce, Prentice-Hall, 2002.

Introductory Techniques for 3D Computer
Vision, Emanuele Trucco and Alessandro Verri,
Prentice-Hall 1998.
5
OpenCV Resources

OpenCV Computer Vision Application
Programming Cookbook Second Editon, Robert
Laganière, Packt Publishing, 2014.

Learning OpenCV, Gary Bradski and Adrian
Kaehler, O'Reilly, 2008.

Mastering OpenCV with Practical Computer
Vision Projects, Daniel Lélis Baggio, et al., Packt
Publishing, 2012.
6
Applications of
Computer Vision
7
Image Stitching
Image Matching
Object Recognition
3D Reconstruction
Interior Modeling
12
3D Augmented Reality
13
3D Camera Tracking
14
Stereo Conversion for 3DTV
15
Depth Estimation and View
Interpolation for 3DTV
16
Human Tracking
17
License Plate Recognition
18
Human Pose Estimation
19
Course Outline
20
Topics to be covered...
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Linear Filters, Frequency Domain
Filtering, Edge and Boundary Detection
Feature Detection
Fitting, Alignment
Histograms
Covariance, Principle Component Analysis (PCA)
Face Detection and PCA
Optical Flow and Motion
Tracking and Mean-Shift
Randomized Decision Trees, Pose Estimation
Bag of Features
Context, Two-View Geometry Summary
21
Relation to
Other Fields
22
Computer Vision
Figure from "Computer Vision: Algorithms and Applications,” Richard Szeliski, Springer, 2010.
23
Computer Graphics
Lights and materials
 Shading
 Texture mapping
 Environment effects
 Animation
 3D scene modeling
 3D character modeling
 (OpenGL)

24
Computer Graphics
25
Image Processing Topics
Resampling
 Enhancement
 Noise filtering
 Restoration
 Reconstruction
 Segmentation
 Image compression
 (MATLAB and OpenCV)

26
Image Processing
27
Video Processing Topics
Motion estimation
 Frame-rate conversion
 Multi-frame noise filtering
 Multi-frame restoration
 Super-resolution
 Video compression
 (MATLAB & OpenCV)

28
Video acquisition-display chain
Capture
Representation
Coding
Transmission
Decoding
Rendering
29
Human vs.
Computer
30
Optical illusions
Actual vs. Perceived
Intensity (Mach band effect)
32
Brightness Adaptation of the Eye
33
Optical illusions
Optical illusions
Why is Computer Vision
Difficult?
Human perception
Human perception
Human Visual
System
40
Human Eye
Photoreceptors: Rods & Cones
Rods vs. Cones

Rods
Perceive brightness only
 Night vision


Cones
Perceive color
 Day vision
 Red, green, and blue cones

Cone Distribution
Blue is less-focused
64%
32%
2%
Visual Threshold drop during
Dark Adaptation
Spatial Resolution of the
Human Eye

Photopic (bright-light) vision:



Approximately 7 million cones
Concentrated around fovea
Scotopic (dim-light) vision


Approximately 75-150 million rods
Distributed over retina
(HDTV: 1920x1080 = 2 million pixels)
49
Frequency Responses of Cones

Same amount of
energy produces
different sensations of
brightness at different
wavelengths

Green wavelength
contributes most to
the perceived
brightness.
50
Trichromatic Color Mixing

C
Any color can be obtained by
mixing three primary colors Red,
Green, Blue (RGB) with the right
proportion
T C ,
k 1, 2 , 3
k
k
Tk : Tristimulu s values
Image
Formation
53
Human Eye vs. Camera
Camera components
Eye components
Lens
Lens, cornea
Shutter
Iris, pupil
Film
Retina
Cable to transfer images
Optic nerve to send the incident
light information to the brain
Human Vision
Image formation
Pin-Hole Camera Model
Point Spread Effect
Out-of-Focus Blur
Shrinking the Aperture
Converging Lens
Correction with a
Converging Lens
Perfectly In-Focus for a
Certain Distance Only
“circle of
confusion”
Depth-of-Field
Depth-of-Field
“Sharp Image” within Depth-ofField due to Finite Sensor Size
ZF
ZN
Focal Length (F)
and Depth (Z)
Z
F
Y
y
Y
yF
Z
xF
X
Z
Aperture Size Affects
Depth-Of-Field
f / 5.6
f / 32
Aperture
Ad
2
Camera f-number
F
f 
d
F
A   
 f 
2
Exposure Time
Motion Blur Effect due to
Finite Exposure Time
Decrease in aperture
implies…
 Increase
in depth-of-field
 Decrease in motion blur
 Decrease in exposure
2D Image
Representation
75
Image Capture
(Courtesy Gonzalez & Woods)
76
Digital Image Capture
Digital Image Capture

Light sensitive
diodes convert
photons to electrons
Color Image Capture:
Single vs. Three CCD Arrays
Bayer filter
(cheaper but introduces
spatial resolution loss)
RGB splitter
(three separate imaging
sensors, higher resolution)
Digital Camera Issues

Noise

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Color


charge overflowing into neighboring pixels
In-camera processing
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color fringing (chromatic aberration) artifacts from Bayer patterns
Blooming

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caused by low light
over-sharpening can produce halos
Compression

creates blocking artefacts
Digitization:
Sampling and Quantization
Sampling Rate Problem
Over Quantization
83
Images as Matrices of
Integers
(0,0)
m
126 127 126 128 127 124 158
125 126 127 123 120 144 163
123 126 125 121 128 155 160
126 123 127 122 142 162 164
120 122 124 130 157 161 166
119 121 123 145 162 164 165
0 → black, 255 → white
n
0 ≤ s(m,n) ≤ 255 } quantization
0 ≤ m ≤ M-1
MxN 8-bit gray-scale (intensity, luminance) image
84
0 ≤ n ≤ N-1
sampling
Images as Functions

We can think of an image as a function, f, from R2 to R:
 f( x, y ) gives the intensity at position ( x, y )
 Realistically, we expect the image only to be defined
over a rectangle, with a finite range:
• f: [a,b]x[c,d]  [0,1]

A color image is just three functions pasted together.
We can write this as a “vector-valued” function:
 r ( x, y ) 
f ( x, y )   g ( x, y ) 


 b( x, y ) 
RGB Color Bands (Channels)
Red
Green
Blue
YUV Bands


Also called Y Cb Cr
Y : Luma
Cb : Chrominance_blue
Cr : Chrominance_red
Color
Y
U
(Cb)
V
(Cr
)
YUV-RGB Conversion
Summary
89
Summary

Human visual system

Pin-hole camera model

Image representation
90