1. Introduction to Computer and Human Vision
Shimon Ullman, Ronen Basri, Michal Irani
Assistants:
Tal Hassner
Eli Shechtman

2. Misc... Course website: www.wisdom.weizmann.ac.il/~hassner/cv0203
To be added to course mailing-list:
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Other recommended courses (for credit):
- Basic Topics
- Statistical Machine Learning
Vision & Robotics Seminar (not for credit):
Thursdays at 11:00-12:00 (Ziskind 1)
send ask to be added to “seminar13” mailing list

3. Applications:
- Manufacturing and inspection; QA
- Robot navigation
- Autonomous vehicles
- Guiding tools for blind
- Security and monitoring
- Object/face recognition; OCR.
- Medical Applications
- Visualization; NVS
- Visual communication
- Digital libraries and video search
- Video manipulation and editing
How is an image formed? (geometry and photometry)
What kind of operations can we apply to images?
What do images tell us about the world? (analysis & interpretation)

4. Tentative Schedule Lessons 1-3 (Michal): Basic Image Processing
Lessons (Ronen): Stereo and Structure from Motion
Lessons (Michal): Motion and video analysis
Lesson (Ronen): Image Segmentation
Lesson (Ronen): Photometry
Lesson (Shimon): Object recognition
Lessons (Shimon): Human Vision
3 programming exercises (MATLAB) CAN SUBMIT IN PAIRS
3-4 theoretical exercises MUST SUBMIT INDIVIDUALLY
EXAM

5. Digital Images today Image Formation:
World
Camera
Digitizer
Digital
Image
Image Formation:
(i) What determines where the image of a 3D point appears on the 2D image?
(ii) What determines how bright that image point is?
(iii) How is a digital image represented?
(iv) Some simple operations on 2D images?
today

6. Digital Images PIXEL World Camera Digitizer Digital Image Typically:
PIXEL
Typically:
0 = black
255 = white
(picture element)

8. Grayscale Image
x = 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
y =

9. Three types of images: Gray-scale images Binary images Color images
I(x,y)  [0..255]
Binary images
I(x,y)  {0 , 1}
Color images
IR(x,y) IG(x,y) IB(x,y)

10. Color Image

11. Effects of down-sampling (reducing number of pixels)

12. Effects of reducing number of gray levels
(8 bits/pixel)
16 gray levels
(4 bits/pixel)
8 gray levels
(3 bits/pixel)
4 gray levels
(2 bits/pixel)
2 gray levels
(1 bit/pixel)
BINARY IMAGE

13. The Image Histogram Histogram = The gray-level distribution:
Occurrence
(# of pixels)
Gray Level
Histogram = The gray-level distribution:
H(k) = #pixels with gray-level k
Normalized histogram: Hnorm(k)=H(k)/N
(N = # pixels in the image)
Continuous probability density function:

14. The Image Histogram (Cont.)
PI(k) 1 k
PI(k) 1
0.5 k
PI(k)
0.1 k

15. Histogram Stretching
PI(k) k
0.1
PI(k) k
0.5
0.1

16. Histogram Equalizationk k k

17. Histogram Equalization
Original
Equalized

18. Histogram Equalization
3000
3000
2500
2500
2000
2000
1500
1500
1000
1000
500
500 50
100
150
200
250 50
100
150
200
250
Original
Equalized

19. Histogram Specification
Transforms an image so that its histogram matches that of another image
(e.g., for comparing two images of the same scene acquired under different lighting condition) Aa Ab k k

20. noisy image (salt & pepper noise)
Image Enhancement
1) Gray value (histogram) Domain
2) Spatial Domain
3) Frequency Domain
- Histogram stretching
- Histogram equalization
- Histogram specification
- Gamma correction
etc...
noisy image (salt & pepper noise)

21. Spatial Operations g(x,y) = 1/M S f(n,m)
Replace center pixel with average/median level:
(averaging mask; weighted mask; median filter…)
Examples of neighborhoods S:
3 x 3
5 x 5
S = neighborhood of pixel (x,y)
M = number of pixels in neighborhood S
e.g.,
g(x,y) = 1/M S f(n,m)
(n,m) in S

22. Noise Cleaning Salt & Pepper Noise 3 X 3 Average 5 X 5 Average
Median

23. Noise Cleaning Salt & Pepper Noise 3 X 3 Average 5 X 5 Average
Median

24. Other spatial filters
Are strong brightness variations always noise…?

25. Edge Detection

28. Edge Types Line Edge Step Edge gray value x edge edge gray value x

29. Edge Detection by Differentiation
gray value
1D image f(x) x
1st derivative f'(x)
threshold
|f'(x)|
Edge Pixels:
|f'(x)| > Threshold

30. Original image
x derivative
y derivative
Gradient magnitude

31. Edge Detection
Image
Vertical edges
Horizontal edges

32. Edge Detection
Image

33. Image Sharpening Blurry Image Laplacian Sharpened Image
Also Laplacian; Zero-crossings; Edge sharpening; etc….

34. The End...
Exercise#1: Noise Cleaning -- on course website (+ Matlab tutorial)
DUE: Nov (in 2 weeks)
Course mailing list:
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Vision & Robotics Seminar:
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36. Panoramic Mosaic Image
Original video clip
Generated Mosaic image

37. Video Removal
Original
Original
Outliers
Synthesized

38. Image Segmentation Note that the camouflaged Squirrel is detected.
The background is still broken due the lack in oriented-texture measurements which we are currently adding into our algorithm.

39. Image Segmentation

40. Photometric Stereo

41. Photometric Stereo