Digital image processing - Download file di http://rumah-belajar.org

1. INTRODUCTION TO IMAGE
PROCESSING AND ANALYSIS
Tati R. Mengko

2. Sinyal dan Informasi
central sulcus
Transmisi sinyal ke otak
motor control touch & pressure
taste
speech
smell
vision
hearing
ACTION
Morphonix LLC, Sausalito, CA

3. Sistem Visual
Zrener E, Science, 295, 1022-1025, (2002), AAAS

4. A picture says more than a thousand words
National Geograhic Traveler Magazine, May/June 2004, Photograph by Justin Guariglia

5. Citra Cahaya Tampak (Visible Light)
Warna
Kandinsky: Almost Abstract Audrey Flack: A Super Realist Still Life
Tekstur
Persepsi Visual

6. Citra Cahaya tak Tampak (Non-visible Light)
Citra inframerah
Citra radar
Citra sinar-X

7. Modalitas Pencitraan
Citra terbentuk ketika sensor menangkap radiasi yang sudah
berinteraksi dengan obyek fisik.
Informasi tentang obyek dalam pandangan direkam sebagai
perubahan dalam intensitas dan warna dari radiasi yang
dideteksi.

8. pencitraan tekstur kulit pencitraan reservoar panas bumi
pencitraan objek astronomi pemetaan deposit minyak bumi
tomografi struktur 3D
pemeriksaan struktur anatomi interior bumi
pencitraan iklim
otomasi industri
robot vision
pencitraan untuk diagnosis Image Processing
Application
reverse engineering
analisis pencemaran
lingkungan
nondestructive testing
deteksi keretakan bangunan
thermal imaging
remote sensing
biometrik
pencitraan struktur material

9. Iris Scan
Differentiation of people uses unique patterns in the iris tissue known as the
trabecular meshwork
Attacks and Countermeasures
Usability

10. the da Vinci System, picture from IntuitiveSurgical.com

11. IMAGE PROCESSING & ANALYSIS SCHEME
Problem Domain:
Intermediate Level Image Processing
Feature Extraction
Pre -
processing
Recognition &
Knowledge base Interpretation Result
Image
Acquisition
Low Level Image Processing High Level Image Processing

12. INTRODUCTION
Image
Processing
Computer Computer
Vision Graphics

13. IMAGE Image IMAGE
Processing
Computer Computer
Vision Graphics
PERCEPTION
Features
Decision
Mathematical model
….
etc

14. Digital Image Processing Components
Communication
Image Acquisition Display
Processing
Storage

15. Part 2:
Image Acquisition

16. IMAGE ACQUISITION

17. IMAGE ACQUISITION

18. Analog vs Digital Image
Analog Image Digital Image

19. Sampling & Quantization
Column of samples
Pixel 255
Black
Line
Line Spacing Gray 128
White 0
Sample Spacing
Picture Sampling process
Brightness Spacing
Spatial resolution
Quantization Process
Brightness Resolution

20. Matrix Representation
•In a (8-bit) greyscale image each picture element has an assigned intensity
that ranges from 0 to 255.
•Each pixel has a value from 0 (black) to 255 (white).

21. Part 3

22. IMAGE PRE-PROCESSING
Image Transform

23. Why Do Transforms?
• Fast computation
• e.g., convolution vs. multiplication
• Conceptual insights for various image processing
• e.g., spatial frequency info. (smooth, moderate change, fast change,
etc)
• Obtain transformed data as measurement
• e.g., medical images
• Need inverse transform

24. Image Transforms
Image transforms a class of unitary matrices used for representing
images.
• Simple arithmetic operations on images or complex mathematical operations
which convert images from one representation to another.
Transform theory has played a key role in image processing for a number
of years.
2-D transforms are used for image enhancement, restoration, encoding,
and description.

25. Image Transforms
Foundations for image transforms
• Matrices
• Unitary transform (and orthogonal transfrom)
• (K-L transform)
Examples of unitary transforms
• Discrete Fourier Transform (DFT)
• Discrete Cosine Transform (DCT)
• Haar transform
• K-L transform

26. DFT Example
50 50
100 100
150 150
200 200
50
250 250
50 100 150 200 250 100 50 100 150 200 250
Original image 150
Phase image
200
250
50 100 150 200 250
Log magnitude of DFT
coefficient

27. DFT Example
Original image Phase
Magnitude Magnitude centered

28. Example of DCT
50
100
150
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100
200
150
250
50 100 150 200 250 50
Original image 200
100
250
50 100 150 200 250 150
DCT coefficient 200
250
50 100 150 200 250
log magnitude of
DCT coefficient

29. Part 4

30. IMAGE PRE-PROCESSING
Image Enhancement

31. Techniques

32. Image Enhancement Characteristics
Definition:
accentuation, sharpening of image features (edge, boundaries, or contrast) to
make a graphic display more useful for display and analysis.
Characteristics:
• does not increase the inherent information content in the data.
• increases the dynamic range of the chosen features so that they can detected easily.
• greatest difficulty: quantifying the criterion for enhancement.

33. Adjusting the Image Histogram to Improve Image Contrast
Poor Contrast Adjusted Image Histogram

34. Median Filter
Noisy Image Median Filtered Image
(Salt & Pepper Noise)

35. Transform Operation
Steps:
1. Convert image into a transform domain representation
2. Process the image in transform domain
3. Inverse-transform the processed image to obtain enhanced version

36. Transform Operation Example
1
Original Image Frequency Domain Image
2 3
Band-reject Filter Filtered Image

37. Part 5

38. IMAGE RECONSTRUCTION

39. Image Reconstruction
Radon Transform
+∞+∞
g (s,θ ) ≡ R ( f ) = ∫ ∫ f (x, y )δ (x− ∞θ<+sy< ∞, 0 ≤)θ < π
−∞−∞
cos sin θ − s dx dy
s
g(s,θ)
y
u
θ
x
f(x,y)

40. Radon Transform
citra phantom asal Has il trans form as i Radon invers e
50
50
100 100
150
150
200 Radon Transform 200
of Head Phantom 250
250
50 100 150 200 250 Using 90 50 100 150 200 250
Projections
Original image has il trans form as i Radon dari c itra phantom
Inverse Radon
-150 60 Transforms of the
-100 50
Shepp-Logan
-50
Head Phantom
40
0
x′
30
50
20
100
10
150
0
0 50 100 150
θ

41. Part 5

42. FEATURE EXTRACTION
Image Segmentation

43. Segmentation Methods
Boundary-based
• Edge detection
Region-based
• Thresholding
1 2

44. Edge Detection Example
Example: Roberts Operator
Original Image

45. Region-based Approach
Groups pixels based on similarity
• Intensity similarity
• Intensity variance similarity
• etc.

46. FEATURE EXTRACTION
Shape Analysis

47. y
RECTANGULARITY
βmax
αmax
θ Rectangularity (Bounding
x
Rectangle)
αmin
smallest rectangle that fits an
βmin object according to its orientation
α = x cosθ + y sin θ
β = − x sin θ + y cosθ
LR = α max − α min AR = LR .WR
WR = β max − β min

48. CIRCULARITY
R
Circularity
• The smallest circle that encloses an
object
• Center of the circle = center of mass
of the object
• Radius = maximum distance between
center and boundary
P2
C=
A

49. SHAPE DESCRIPTOR
SPHERICITY
• Ratio between smallest and biggest circle radius which
are centered in the center of mass of the object.
• 0 ≤ spher ≤ 1
• Circle: spher = 1
Rc
rinscribing ( Ri )
spher =
rcircumscribed ( Rc ) +
Center of Mass Ri

50. FEATURE EXTRACTION
Texture Analysis

51. Texture
Texture:
A global pattern arising from the repetition, either deterministically or randomly,
of local sub-patterns.
Macrostructure
Microstructure

52. 1st Order Features
Based on image histogram characteristics
Statistical Features:
• Mean
• Variance
• Skewness
• Curtosis
• Entropy

53. Texture Isolation with Gabor Filter
Θ = 0o Θ = 30o
30
Frequency=√2/ Θ = 60o
60 Θ = 90o
90
23

54. Part 6

55. CLASSIFICATION

56. Problem
Sumo Wrestler
Weight
Runner
Horse-rider
Height

57. Approaches
Statistical Classification
• Bayesian Classifier
Syntactical Classification
• Rule-based Classification
Unsupervised Learning Approach
• Neural Network

58. Examples
Statistical Classification
ω1 = class-1
ω2 = class-2
x = classification object

59. Examples
Neural Network Diagram
Features Input Classification Output

60. References
S. Webb Ed., The Physics of Medical Imaging, Medical Science Series
Z-H Cho, JP Jones, & M. Singh, Foundations of Medical Imaging, Wiley
Z-P. Liang and Lauterbur, Principles of Magnetic Resonance Imaging: A
Signal Processing Perspective, IEEE Press, 2000.
AK Jain, Fundamentals of Digital Image Processing, PHI
RC. Gonzalez & RE Woods, Digital Image Processing, Pearson Education
Any sources from the internet.