Top Banner
VENUS TRANSIT 2004 and IMAGE PROCESSING Stanislava ˇ Simberov´ a mailto:[email protected] May 7, 2004
47

VENUS TRANSIT 2004 and IMAGE PROCESSING

Feb 03, 2022

Download

Documents

dariahiddleston
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: VENUS TRANSIT 2004 and IMAGE PROCESSING

VENUS TRANSIT 2004

and IMAGE PROCESSING

Stanislava Simberova

mailto:[email protected]

May 7, 2004

1

Page 2: VENUS TRANSIT 2004 and IMAGE PROCESSING

OUTLINE

• Goals of Image Processing in the VT-2004 Project

2

Page 3: VENUS TRANSIT 2004 and IMAGE PROCESSING

OUTLINE

• Goals of Image Processing in the VT-2004 Project

• Skeleton of the Pipeline

2

Page 4: VENUS TRANSIT 2004 and IMAGE PROCESSING

OUTLINE

• Goals of Image Processing in the VT-2004 Project

• Skeleton of the Pipeline

• Image Enhancement

2

Page 5: VENUS TRANSIT 2004 and IMAGE PROCESSING

OUTLINE

• Goals of Image Processing in the VT-2004 Project

• Skeleton of the Pipeline

• Image Enhancement

• Image Restoration and Analysis

2

Page 6: VENUS TRANSIT 2004 and IMAGE PROCESSING

OUTLINE

• Goals of Image Processing in the VT-2004 Project

• Skeleton of the Pipeline

• Image Enhancement

• Image Restoration and Analysis

• Aditional Mathematical Operation and Distance Computation

2

Page 7: VENUS TRANSIT 2004 and IMAGE PROCESSING

DIGITAL IMAGE PROCESSING

is used for three fundamental purposes:

-improving the visual appearance of images to a human viewer

3

Page 8: VENUS TRANSIT 2004 and IMAGE PROCESSING

DIGITAL IMAGE PROCESSING

is used for three fundamental purposes:

-improving the visual appearance of images to a human viewer

-preparing images for further analysis

3

Page 9: VENUS TRANSIT 2004 and IMAGE PROCESSING

DIGITAL IMAGE PROCESSING

is used for three fundamental purposes:

-improving the visual appearance of images to a human viewer

-preparing images for further analysis

-investigating hidden information in the image

3

Page 10: VENUS TRANSIT 2004 and IMAGE PROCESSING

In the image processing chain:

4

Page 11: VENUS TRANSIT 2004 and IMAGE PROCESSING

In the image processing chain:

• menu of the methods

4

Page 12: VENUS TRANSIT 2004 and IMAGE PROCESSING

In the image processing chain:

• menu of the methods

• INFO + EXAMPLES

4

Page 13: VENUS TRANSIT 2004 and IMAGE PROCESSING

In the image processing chain:

• menu of the methods

• INFO + EXAMPLES

• application to the real image fits, gif, jpeg

4

Page 14: VENUS TRANSIT 2004 and IMAGE PROCESSING

USER REGISTRATION

NAME

PASSWD

REGISTRATION CONFIRMED

MAIN PAGE MENU

e−mail

5

Page 15: VENUS TRANSIT 2004 and IMAGE PROCESSING

link to http:// ...

link to http:// ...

link to http:// ...

TENERIFE

MAIN PAGE MENU

ESO

ONDREJOV

PARIS

...etc.

//asu.cas.cz/~sunwatch

Professional observatories

6

Page 16: VENUS TRANSIT 2004 and IMAGE PROCESSING

http://proxyon.asu.cas.cz/~venus

− amateur observations

.....

A 4

A 2

A 3

A 1

PLACES of observation

MAIN PAGE MENU

7

Page 17: VENUS TRANSIT 2004 and IMAGE PROCESSING

MAIN PAGE MENU

Aditional math. operations

Image ANALYSIS

Image ENHANCEMENT

8

Page 18: VENUS TRANSIT 2004 and IMAGE PROCESSING

MENU − processing methods

IMAGE ENHANCEMENT

HISTOGRAM modification

CONTRAST manipulation

NOISE cleaning

EDGE crispening

INFO

INFO

INFO

INFO

Example

Example

Example

Example

9

Page 19: VENUS TRANSIT 2004 and IMAGE PROCESSING

MENU − processing methods

INFO

INFO

INFO

INFO

INFO

Example

INFO

Example

Example

Example

Example

Example

IMAGE ANALYSIS

EDGE detection PREWITT

SOBEL

CONE

LAPLACE 2

ISOTROPIC

LAPLACE 1

10

Page 20: VENUS TRANSIT 2004 and IMAGE PROCESSING

Example

Example

Example

Example

ExampleINFO

INFO

INFO

Aditional math. operation

INFO

INFO

MENU − processing methods

DISTANCE computation

Subtraction

Addition

Multipl./divis.

Statistics

Histogram

11

Page 21: VENUS TRANSIT 2004 and IMAGE PROCESSING

Bad examples

DISTANCE COMPUTATION

Data acquisition page

insert image + text.file

OBSERVATION

Demands for observ.

observatoryOWNONDREJOV

FULL DISCOBSERVATION in

DISTANCEVenus (barycenter)

SUN (limb or center)

H and white lightα

12

Page 22: VENUS TRANSIT 2004 and IMAGE PROCESSING

V

T

ra

t

v

sy

y

y

x x xt v s

a = r (x −x ) / (x − x ) = r (y −y ) / (y − y ) v t s t v t s t

S

� � � � �� � � � �� � � � �� � � � �� � � � �� � � � �

� � � � �� � � � �� � � � �� � � � �� � � � �� � � � �

������

� � � � �� � � � �� � � � �� � � � �� � � � �� � � � �

� � � �� � � �� � � �� � � �� � � �� � � �

�������������

�������������

+

13

Page 23: VENUS TRANSIT 2004 and IMAGE PROCESSING

EXAMPLES OF THE NON−ACCEPTED IMAGES

SOLAR LIMB

VENUS

14

Page 24: VENUS TRANSIT 2004 and IMAGE PROCESSING

IMAGE ENHANCEMENT and ANALYSIS

• consisting of various techniques that seek to improve the

visual appearance of an image

15

Page 25: VENUS TRANSIT 2004 and IMAGE PROCESSING

IMAGE ENHANCEMENT and ANALYSIS

• consisting of various techniques that seek to improve the

visual appearance of an image

• preprocessing methods to prepare an image for analysis

15

Page 26: VENUS TRANSIT 2004 and IMAGE PROCESSING

IMAGE ENHANCEMENT and ANALYSIS

• consisting of various techniques that seek to improve the

visual appearance of an image

• preprocessing methods to prepare an image for analysis

• the basis of linear filtering is convolution teorem

g(x, y) = f(x, y) ∗ h(x, y)

15

Page 27: VENUS TRANSIT 2004 and IMAGE PROCESSING

IFFTFFT

domain

Transform

domain

Spatial

Basic Scheme of Digital Image Processing

f (x,y) g (x,y)

transform domain

spatial domain in spatial domain in spatial domain

domain

Discrete image in Processing Processed image in

in transform transform domain

Discrete image in Processing Processed image

F (u,v) G (u,v)

16

Page 28: VENUS TRANSIT 2004 and IMAGE PROCESSING

f (x, y) h (x, y) <=> F(u, v) H (u, v)

f (x, y) h (x, y) <=> F(u, v) H (u, v)

Σ Σ

C = ( L + 1 ) / 2

m n

− impulse response array L x L size

F ( j,k ) − input image

where G ( j,k) − filtered output image

H ( j,k )

G ( j,k ) = F ( m,n ) H ( m−j−C, n−k+C )

The discrete convolution equation

✴.

.

17

Page 29: VENUS TRANSIT 2004 and IMAGE PROCESSING

ΣΣ

h(1,1) h(1,0) h(1,−1)

h(0,1) h(0,0) h(0,−1)

h(−1,1) h(−1,0) h(−1,−1)

f(x+1,y−1) f(x+1,y) f(x+1,y+1)

f(x−1,y−1) f(x−1,y) f(x−1,y+1)

f(x,y−1) f(x,y) f(x,y+1)

y

Image f(x,y)

Image matrix

x

g(x,y) = h(k,l)f(x−k,y−l)

the 3x3 mask and the corresponding image under it.

Illustration of two−dimensional convolution −

Mask coefficients

Pixels of image under mask

Mask

18

Page 30: VENUS TRANSIT 2004 and IMAGE PROCESSING

of density, but the details are preservedto enhance contrast by the uniform distribution

− based on cumulative histogram

EQUALIZATION

HISTOGRAM modification

different range of density

f 1, f 2, ...., f 8

special transfer functions

CONTRAST manipulation

19

Page 31: VENUS TRANSIT 2004 and IMAGE PROCESSING

Masks of the high−pass filters, Sharp, Point, Tent, ...

E 1, E 2, .... , E 5

convolution with HIGH −PASS FORM of the impulse response

EDGE crispening

LOW − PASS FORM of the impulse response

N 1, N 2, ....., N 9Smoothing, Median, Gauss, Min, Max, ...

photometric reproduction

performed on local neighborhoods of input pixel

an image with accentuated edges is more pleasing than exact

cleaning algorithms are based on spatial operations

additive noise −> discrete isolated pixel variations

NOISE cleaning

20

Page 32: VENUS TRANSIT 2004 and IMAGE PROCESSING

D 4, D 5, D 6 occurs in the 2nd derivative.An edge is marked if a significant spatial change

(4, 8 neighbor; Laplacian of Gaussian)LAPLACE

the second order derivative

directions. D 1, D 2, D 3involve generation of gradients in two orthogonal

ROBERTS, PREWITT, SOBEL, FREI−CHEN, ...

the first order derivative of an image function

Methods based on

.against a light backgroundEdge, line and spot locations are specified by dark pixels

Edges characterize object boundaries.

image description, segmentation, scene analysisdata exctraction,

EDGE detection

IMAGE ANALYSIS

21

Page 33: VENUS TRANSIT 2004 and IMAGE PROCESSING

22

Page 34: VENUS TRANSIT 2004 and IMAGE PROCESSING

23

Page 35: VENUS TRANSIT 2004 and IMAGE PROCESSING

24

Page 36: VENUS TRANSIT 2004 and IMAGE PROCESSING

25

Page 37: VENUS TRANSIT 2004 and IMAGE PROCESSING

26

Page 38: VENUS TRANSIT 2004 and IMAGE PROCESSING

27

Page 39: VENUS TRANSIT 2004 and IMAGE PROCESSING

28

Page 40: VENUS TRANSIT 2004 and IMAGE PROCESSING

Prewitt operator

29

Page 41: VENUS TRANSIT 2004 and IMAGE PROCESSING

Prewitt operator

30

Page 42: VENUS TRANSIT 2004 and IMAGE PROCESSING

Sobel operators horizontal, vertical

31

Page 43: VENUS TRANSIT 2004 and IMAGE PROCESSING

Result of Sobel operators

32

Page 44: VENUS TRANSIT 2004 and IMAGE PROCESSING

Prewitt operators horizontal, vertical

33

Page 45: VENUS TRANSIT 2004 and IMAGE PROCESSING

Result of Prewitt operators

34

Page 46: VENUS TRANSIT 2004 and IMAGE PROCESSING

Result of Laplace1 operator

35

Page 47: VENUS TRANSIT 2004 and IMAGE PROCESSING

IN ASTRONOMY YOUR IMAGE IS EVERYTHING.

TEN THOUSAND WORDS.

Anonymous

ONE PICTURE IS WORTH MORE THAN

36