Simple Image Processing Speaker : Lin Hsiu-Ting Date : 2005 / 04 / 27.
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Transcript of Simple Image Processing Speaker : Lin Hsiu-Ting Date : 2005 / 04 / 27.
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Simple Image Processing
Speaker : Lin Hsiu-Ting
Date : 2005 / 04 / 27
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Outline
Concept of Image Processing
Space Domain Image Processing
Frequency Domain Image Processing
Geometry Transform
Shape Processing
Color System
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Concept of Image Processing
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Concept
The “Image” Signals We Can See Include Special
Information We Process These Signals To Get Relative
Information Integration Technology
Engineering Mathematics Physical Biology Medical Science Entertainments
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Concept
Application Digital Photo Map Natural Disaster Monitored Others…
Relative Software Photo Shop Photo Impact Others… These Aren’t Today Key Points
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Concept
General Topics of Image Process Image Capture & Image Digitize Image Stretch & Remove Distortion Shape Process Image Features Extracted Color Image Process Image Coding & Compression
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Concept
Image Digitized Sampling Quantization Coding
Non-Ideal Situations In Process Quantization Error Distortion Noise
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Image with Noise
Images Usually Suffer Noise When Sampling
(Like Use Scanners or Digital Cameras…) Some Common Noise
Dot Noise Uniform Noise Sinusoid Wave Noise Gaussian Noise Other
Sometimes We Can Remove Noise According Their Features
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Image with Noise
Dot Noise
Uniform Noise
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Image with Noise
Sinusoid Wave Noise
Gaussian Noise
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Space Domain Image Processing
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Space Domain Image Processing
Characteristic Representation Profile Histogram Statistic ( Mean & Standard Deviation )
Point Operation Binarization Inverse Contract Stretch Histogram Equalization Gamma Correction Arithmetic & Logic Operation
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Binarization Before Binarization ( 8-bit Gray Level )
Binarization (Threshold = 200)
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Contract Stretch
Before Processing
After Processing
Process Flow
Load Image
Histogram
Statistic
Stretch
n
MinMax
MinffT 2)(
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Histogram Equalization
Before Processing
After Processing
Process Flow
Load Image
Histogram
Statistic
Equalization
n
iin fpfT
0
)()(
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Arithmetic (Add & Sub) Image #1
Image #2
Image #1 + Image #2
Image #1 - Image #2
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Space Domain Image Processing
Range Operation Smoothing ( Low Pass Filter ) Median Filter High Pass Filter Differentiation
Mask Matrix
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Note : We Can Also Use 5x5 , 7x7 or Larger Matrix Process Range Operation But It Cause More Computing
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Median Filter
Before Processing
After Processing
For Every 3 x 3 Block
Search Cn = Median (C)
Let f (x , y) = Cn
Note : The Method Will Have Poor Result When A Lot Of Noise Cluster
987
654
321
CCC
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Frequency Domain Image Processing
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Frequency Domain Image Processing
Fast Fourier Transform
Implement Recursion Algorithm
Butterfly Algorithm
Easy To Achieve Filter High Pass / Low Pass
Band Pass / Notch
1
2
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02/ ]12[]2[][
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kN
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Frequency Domain Image Processing
2D Fast Fourier Transform
)),(((),( yxfFFTFFTvuF vyux
Do FFT For Every Row
……………..
.................
Do FFT For Every Column
F ( u , v )
Note : We Always Use Log Unit Present The Spectrum Distribute Instead of Linear Because Its Dynamic Range is Larger Then Screen
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Frequency Domain Image Processing
Image
Spectrum
Image with Sin Noise
Spectrum
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Geometry Transform
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Geometry Transform
Coordinates Transform Rotation
Scaling
Twist
Gray Level Interpolation Replicative Interpolation
Bilinear Interpolation
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Coordinates Transform
Rotation
Scaling
Twist
yCosxSiny
ySinxCosx
'
'
byy
axx
'
'
yy
yTanxx
'
'
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Gray Level Interpolation
When We Transform From R to R* Some Point In R* Can’t Correspond From R Rotation, Magnify Suffer This Question Ex: Magnify
1 2 3
4 5 6
7 8 9
1 ? 2 ? 3 ?
? ? ? ? ? ?
4 ? 5 ? 6 ?
? ? ? ? ? ?
7 ? 8 ? 9 ?
? ? ? ? ? ?
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Gray Level Interpolation
Replicative Interpolation Use The Nearest Point To Present
Let j = Int(x+0.5) , k = Int(y+0.5) =>
g ( x’ , y’ ) = f ( j , k )
Bilinear Interpolation Use Four Neighborhood Points More Smooth Than Replicative
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Gray Level Interpolation Replicative Interpolation
Bilinear Interpolation
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Shape Processing
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Shape Processing
Find The Edges And Bones Binarization
Process The Edge And Bone Erosion Dilation Open / Close Remove Isolate Points
Usually Simple Logic Operation
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Erosion & Dilation
Binarization Image Erosion
Dilation
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Color System
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Color System
The Colors We See Wave Length 380 nm ~ 780 nm Use Rods to Recognize Brightness Use Cones to Recognize Colors
(Three Types For R. G. B. Colors) Usually Eyes Are More Sensitive To
Brightness Than Colors This Feature is Convenient For Image
Compressing
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Color System
Common Color System
R. G. B. System (Red, Green and Blue)
C. M. Y. System (Cyan, Magenta and Yellow)-- A Complement of R. G. B
Y. U. V System
Y. I. Q System
H. S. I. System
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Conclusion
Image Processing Is Useful Image Processing Is Interesting Although We Needn’t Know The
Details Of Techniques Because Many Powerful Software Will Handle Them…
But Knowing General Concept Is Helpful For Us
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Reference
數位影像處理 - 連國珍 著 , 儒林出版 http://www.cs.ecnu.edu.cn/teach/down
/dip/Chapter02.pps http://www.fosu.edu.cn