1. Digital Image Processing 0909.452.01/0909.552.01 Fall 2001
Lecture 3 September 24, 2001
Shreekanth Mandayam
ECE Department
Rowan University

2. Plan Pixel Operations Point processing Histogram equalization
Connectivity
Image Enhancement
Spatial Filtering
Detection of Discontinuities
Edge detection (Sobel, Prewitt and Laplacian masks)
individual pixels
all pixels
neighboring pixels
Low-pass
High-pass

3. DIP: Details

4. Pixel Connectivity x (x-1,y-1) (x,y-1) (x+1,y-1) y (x-1,y) (x,y)

5. Labeling of Connected Components
Begin scan
Update
position (x,y)
Position: (x,y)
p(x,y) = 1?
All positions
scanned? y
p(x-1,y) = 1? y
class(x,y) = class(x-1,y)
p(x,y-1) = 1? y
class(x,y) = class(x,y-1) y
End scan
p(x-1,y) AND
p(x,y-1) = 1? y
class(x-1,y)
= class(x,y-1)
class(x,y) = new class

6. Image Preprocessing Enhancement Restoration Inverse filtering
Wiener filtering
Spatial
Domain
Spectral
Domain
Filtering
>>fft2/ifft2
>>fftshift
Point Processing
>>imadjust
>>histeq
Spatial filtering
>>filter2

7. Spatial Filtering (Masking)
Portion of
a digital image
Mask z1 z2 z3 z4 z5 z6 z7 z8 z9 w1 w2 w3 w4 w5 w6 w7 w8 w9
Replace
with R
= w1z1 + w2z2 + ….. +w9z9

8. Low-pass Filters Moving Average Filter 1 (1/9)* Median Filter z1 z2 z3
Replace
with R
= median(z1, z2 , ….. , z9)

9. Noise Models SNRg = 10log10(Pf/Pn) SNRg = 10log10(sf2/ sn2) f(x,y)
g(x,y)
n(x,y) S
Degradation Model: g = f + n
SNRg = 10log10(Pf/Pn)
Power Variance (how?)
SNRg = 10log10(sf2/ sn2)

10. Noise Models N(0,1): zero-mean, unit-variance, Gaussian RV Theorem:
N(0,s2) = sN(0,1)
Use this for generating normally distributed r.v.’s of any variance
>>imnoise
>>nrfiltdemo
>>filter2
demos/demo2spatial_filtering/lowpassdemo.m

11. High-pass Filters Basic HP Filter -1 8 (1/9)* Gradient Filter z1 z2 z3-1 1 -1
demos/demo2spatial_filtering/highpassdemo.m

12. Detection of Discontinuities-1 8
Point Detection
Line Detection (Prewitt’s Gradient) -1 1 -1 1 2
demos/demo2spatial_filtering/prewitt.m

13. Edge Detection Sobel Masks -1 -2 1 2 -1 1 -2 2 >>edgedemo1 2 -1 1 -2 2
>>edgedemo
>>edge
demos/demo2spatial_filtering/edgegradientdemo.m

14. Lab 1: Pixel Operations

15. Summary