Download presentation

Presentation is loading. Please wait.

Published byCristobal Gallimore Modified over 2 years ago

1
DREAM PLAN IDEA IMPLEMENTATION 1

2
2

3
3 Introduction to Image Processing Dr. Kourosh Kiani Email: kkiani2004@yahoo.comkkiani2004@yahoo.com Email: Kourosh.kiani@aut.ac.irKourosh.kiani@aut.ac.ir Email: Kourosh.kiani@semnan.ac.irKourosh.kiani@semnan.ac.ir Web: www.kouroshkiani.comwww.kouroshkiani.com Present to: Amirkabir University of Technology (Tehran Polytechnic) & Semnan University

4
Lecture 04 4

5
Algebraic operations used for images are commonly viewed in two groups; mathematical and logical operations. Image adding, subtracting, dividing and multiplying operations constitute mathematical processing and “AND, OR, NOT” etc. operations forms logical operations. SIMPLE ALGEBRAIC OPERATIONS in IMAGES

6
……… …a4a3 …a2a1 ……… …b4b3 …b2b1 a4 +b4 a3 +b3 a2 +b2 a1 +b1 + = ……… …a4a3 …a2a1 a4 +10 a3 +10 a2 +10 a1 +10 + = 10

8
Reduce noise (increase SNR) averaging, smoothing... 8 + =

9
I = imread(‘rice.tif’); J = imread(‘cameraman.tif’); K = imadd(I, J); imshow(K) Or i=imread('rice.png'); j=imread('cameraman.tif'); k=i+j; imshow(k);

10
I = imread('kourosh.jpg'); figure(1); imshow(I); I2 = imadd(I, 70); figure(2); imshow(I2); + 70 =

11
Image Averaging Consider a noisy image g(x,y) formed by the addition of noise (x,y) to an original image f(x,y) g(x,y) = f(x,y) + (x,y)

12
Image Averaging If noise has zero mean and is uncorrelated then it can be shown that = image formed by averaging K different noisy images

13
Image Averaging Then = variances of g and Then if K increase, it indicates that the variability (noise) of the pixel at each location (x,y) decreases.

14
Average multiple images (frames) of the same scene together Useful for removing noise + +... =

15
X = 255 10 75 44 225 100 Y = 50 50 50 Z = 205 0 25 0 175 50 X = uint8([ 255 10 75; 44 225 100]); Y = uint8([ 50 50 50; 50 50 50 ]); Z = imsubtract(X,Y) Image Subtracting

16
rice = imread('rice.png'); figure (1) imshow(rice); background = imopen(rice, strel('disk', 15)); figure (2) imshow(background); rice2 = imsubtract(rice, background); figure (3) imshow(rice2); - =

17
- 70 = I = imread('kourosh.jpg'); J = imsubtract(I,70); Figure(1), imshow(I), Figure(2), imshow(J) Image Subtracting

18
_ = Digital subtraction angiography (DSA) Image Subtracting

19
Digital subtraction angiography (DSA) Image Subtracting

20
X = 2 10 7 4 25 10 Y = 5 5 5 3 5 6 Z = 10 50 35 12 125 60 X = uint8([ 2 10 7; 4 25 10]); Y = uint8([ 5 5 5; 3 5 6 ]); Z = immultiply(X,Y) Image Multiplying

21
I = imread('moon.tif'); figure(1) imshow(I) J = immultiply(I,0.5); figure(2) imshow(J) * 0.5 =

22
X = 100 20 75 30 25 36 Y = 5 5 5 3 5 6 Z = 20 4 15 10 5 6 X = uint8([ 100 20 75; 30 25 36]) Y = uint8([ 5 5 5; 3 5 6 ]) Z = imdivide(X,Y) Image Dividing

23
I = imread('rice.png'); figure(1), imshow(I); background = imopen(I,strel('disk',15)); figure(2), imshow(background); Ip = imdivide(I,background); figure(3), imshow(Ip, []); ÷ =

24
Image Dividing ÷ 2 = I = imread('rice.png'); J = imdivide(I,2); figure(1), imshow(I) figure(2), imshow(J)

25
Questions? Discussion? Suggestions ?

26
26

Similar presentations

OK

Automation and Drives SIMATIC HMI The Human Machine Interface for internal use only Scope of Presentation Dept. of Industrial Electronics and Control Eng.

Automation and Drives SIMATIC HMI The Human Machine Interface for internal use only Scope of Presentation Dept. of Industrial Electronics and Control Eng.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on mobile computing pdf Animated ppt on magnetism perfume Ppt on effect of global warming on weather Ppt on single phase and three phase dual converter travel Ppt on different types of dance forms of indian Ppt on albert einstein Ppt on indian textile industries chicago Ppt on object-oriented programming Ppt on history of badminton and rules Ppt on body language