1 scientific data computing mtat.08.042 lecture 4 … · 2016. 5. 2. · lecture 4 image processing...

LECTURE 4 IMAGE PROCESSING AND

ANALYSIS

SCIENTIFIC DATA COMPUTING MTAT.08.042

1

Prepared by:

Amnir Hadachi

Institute of Computer Science, University of Tartu

[email protected]

LECTURE 4: IMAGE PROCESSING AND ANALYSIS

OUTLINE

▸ Introduction

▸ Image acquisition

▸ Image processing

▸ Image enhancement

▸ Image restoration

▸ morphological processing

▸ Image analysis

▸ Segmentation

▸ Object recognition

▸ Representation and description

▸ Image compression

▸ Color images

2

INTRODUCTION1.

3


INTRODUCTION

▸ An image !!?

▸ Reminder: digitalisation implies that a digital image is an approximation of the real scene.

▸ Real scene is continues

▸ Approximation is discontinues or discrete

1 pixel

4


INTRODUCTION

▸ Digital image:

▸ a digital image is a representation of two dimensional image as a finite set of digital values (or pixels)

Digital image = 2 dimensional array of pixels.

Each pixel has an :

Intensity value (illustrated by a digital number)

Location address (represented by row and column numbers)

f(x, y) = I(x,y) I 2 R+

5


INTRODUCTION

▸ Digital image:

▸ A colour image is three function pasted together.

▸ we can write it in a vector representation as follows:

f(x, y) =

2

4r(x, y)g(x, y)b(x, y)

3

5

6


KEY STEPS IN IMAGE PROCESSING

IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION

MORPHOLOGICAL PROCESSING

SEGMENTATION

OBJECT RECOGNITION

REPRESENTATION & DESCRIPTION

Problematic

COLOUR IMAGE PROCESSING

IMAGE COMPRESSION

7

IMAGE ACQUISITION 2.

8



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

9



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

f(x, y) = r(x,y) ⇤ I(x,y)ILLUMINATIONREFLECTANCE

r 2 [0, 1] I 2 R+

10



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

CCD sensor:

charge-coupled device (CCD) is a device designed for the movement of electrical charge within the device capable of connecting it into digital values.

11



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

12



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

A D

8 Bit Greyvalue Volts

0...255

Greyvalue

Volts

0,7

255

0,348

127

brighter

13



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Greyvalue

Volts

0,7

255

0,348

127

brighter

camera image 8 bit grayscale digital image

Pixel mask

14



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Greyvalue

Volts

0,7

255

0,348

127

brighter

255 255 255 255 253 88 74 73 72 72 75 175 255 255 255255 255 255 255 250 82 75 74 73 74 73 190 255 255 255255 255 255 255 231 80 73 72 72 72 76 197 255 255 255255 255 255 255 232 83 73 73 73 76 75 172 255 255 255255 255 255 255 226 79 75 74 74 76 75 184 255 255 255255 255 255 255 220 84 75 73 76 79 74 159 255 255 255255 255 255 255 224 83 76 74 77 75 75 156 255 255 255255 255 255 255 207 90 75 76 78 77 81 172 255 255 255255 255 255 255 252 107 75 75 80 79 79 162 255 255 255255 255 255 255 249 136 77 76 89 81 99 217 255 255 255255 255 255 255 255 183 78 75 80 81 120 248 255 255 255255 255 255 255 255 249 86 76 74 84 201 255 255 255 255255 255 255 255 255 255 115 77 77 98 251 255 255 255 255255 255 255 255 255 255 193 80 78 143 255 255 255 255 255255 255 255 255 255 255 217 85 78 173 255 255 255 255 255255 255 255 255 255 255 248 97 79 220 255 255 255 255 255255 255 255 255 255 255 255 119 80 224 255 255 255 255 255255 255 255 255 255 255 255 255 255 255 255 255 255 255 255255 255 255 255 255 255 255 255 255 255 255 255 255 255 255255 255 255 255 255 255 255 255 255 255 255 255 255 255 255255 255 255 255 255 255 255 255 255 255 255 255 255 255 255255 255 255 255 255 255 255 255 255 255 255 255 255 255 255

Grayscale image and digital representation:

15

IMAGE PROCESSING 3.

16



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

17



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

The purpose behind it:

To bring out specific features of an image

Highlight certain characteristics of an image

Bring up the image into a suitable results convenient for specific application

18



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Main categories behind enhancement:

Spatial domain method

Frequency domain method

Combination of the two methods

is the art of direct manipulation of the image pixels

is the art modifying the frequency and transforming the image

19



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Spatial domain method:

usable for,

Contrast and dynamic range modification

Noise reduction

Edge enhancement and detection

20



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION


g(x, y) = T [f(x, y)]

T: operator on f defined over some neighbourhood of (x,y)

f: input, g:output

21



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION


e.g: applying filters, point processing, mask processing

illustration sharpening filter

K = 1/9

2

4�1 �1 �1�1 9 �1�1 �1 �1

3

5

22



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION


illustration sharpening filter

Applying the filter

x2

4�1/9 �1/9 �1/9�1/9 1 �1/9�1/9 �1/9 �1/9

3

5

S =X

i,j

I(i,j)k(i,j)

S

23



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION


illustration of applying filter to the image

24



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Frequency domain method:

The idea behind is a straight forward:

Compute Fourier transform of the image

Multiply the results by a filter

and take the inverse transform to produce the enhanced image

25

Very useful for reducing the intensity variation across the image while highlighting details.



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

26



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Restoration:

Restoration of degradation

quantifying performance

recovering the original image

27



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

DEGRADATION FUNCTION H

RESTORATION FILTER

+f(x,y)

n(x,y)

f(x,y)^

g(x,y)

g(x,y) = h(x,y)*f(x,y)+n(x,y)

we can describe also the formula using its spectral form by applying Fourier transform.

G(u,v) = H(u,v)F(u,v)+N(u,v)

28



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

• Let’s assume that the degradation is only due to additive noise; Thus, H=1

• Source of degradation will be noise such as:

• First group due to the sensor:

• Light level / Electronic circuits / Temperature

• Second group due to the environment

• Atmospheric disturbance / Lightening / Electro magnetic signals

RESTORATION FILTER

+f(x,y)

n(x,y)

f(x,y)^

g(x,y)

29



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Common noise models:

• Gaussian noise

• Salt and pepper noise

• uniform noise

• Gamma noise

• Exponential noise

• Periodic Noise

30



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Gaussian noise is caused by random fluctuation in the signal. its modeled by random values added to an image:

Original Additive Gaussian noise

31



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Gaussian noise is caused by random fluctuation in the signal. its modeled by random values added to an image:

p(z) =1p2⇡�

e

�(z�µ)2/2�2

z : gray level

µ : mean of average value of z

� : standard deviation of z

32



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Salt and pepper noise usually caused by a sharp & sudden disturbances in the image signal (its appearance on the image is in the form of scattered white or/and black pixels over the image):

33



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Salt and pepper noise usually caused by a sharp & sudden disturbances in the image signal (its appearance on the image is in the form of scattered white or/and black pixels over the image):

p(z) =

8><

>:

Pa for z = a

Pa for z = b

0 otherwise

34



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Uniform noise :

35



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Periodic noise is a summation of sinusoidal signals with the same amplitudes with fixed phases or random phases :

solution we can apply Fourier transform to detect the noise and filter it.

noise component

n(x, y) = ↵cos(x+ y) + �sin(x+ y)

36



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Restoration techniques :

Spatial filtering it is similar to enhancement in spatial domain

3x3 filter GOOD ONLY WHEN THERE IS ADDITIVE NOISE

37



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION


Spatial filtering has three main categories:

1. Mean filters

2. Order statistics filters

3. Adaptive filters

38



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Mean filters

Arithmetic mean filter

Geometric mean filter

Harmonic mean filter

Contra-harmonic mean filter

39



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Mean filters

Arithmetic mean filter

1/9 1/9 1/9

1/9 1/9 1/9

1/9 1/9 1/9

Smooth local variation in an image

Noise is reduced as a result of blurring

f̂(x, y) =1

mn

X

(s,t)2S

xy

g(s, t)

S

xy

: set of coordinates in a rectangular

subimage window of size mxn

g(x, y) : degraded image

40



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Mean filters

Geometric mean filter Similar to arithmetic mean filter but retains image detail better

Achieve smoothing

f̂(x, y) =hQ

(s,t)2Sxy

g(s, t)i 1

mn

41



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Mean filters

Harmonic mean filter

Useful for image with Gaussian or salt noise

Black pixels or pepper noise are not filtered

f̂(x, y) =mnP

(s,t)2Sxy

g(s, t)

42



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Mean filters Contra-harmonic mean filterReduce the effects of salt&pepper noise

for Q>0 eliminate pepper noise

for Q<0 eliminate salt noise

for Q=0 arithmetic mean filter

for Q=1 harmonic mean filter

unable to eliminate salt&pepper noise simultaneously

f̂(x, y) =

P(s,t)2S

xy

g

Q+1(s, t)P

(s,t)2Sxy

g

Q(s, t)

43



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Order statistics filters

Median filter

Max and min filters

Mid-point filter

alpha-trimmed filters

44



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION




Median filter

f̂(x, y) = median(s,t)2Sxy

[g(x, y)]

Output is based on ordering the pixels in a sub image

Replacing the value of a pixel by the median of the gray levels in the neighborhood of that pixel

Effective against removing bipolar and unipolar impulse noise

45



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION




Max and min filters

f̂(x, y) = max(s,t)2Sxy

[g(x, y)]

f̂(x, y) = min(s,t)2Sxy

[g(x, y)]

Max filter:

Replace the value of a pixel by the maximum of the gray levels

Min filter:

Replace the value of a pixel with the minimum of the gray levels

46



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION




Mid-point filter

f̂(x, y) = 1/2⇥max(s,t)2S

xy

[g(x, y)] +min(s,t)2Sxy

[g(x, y)]⇤

Calculate the average of the highest and lowest pixel value within the window

Combine order statistics and averaging

very good against randomly distributed noise like Gaussian and uniform noise

47



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION




alpha-trimmed filters

f̂(x, y) =1

mn� d

X

(s,t)2Sxy

gr(s, t)

Average of gray levels of the remaining (mn-d) pixels in the mask after removing the d/2 lowest and the d/2 highest gray levels in Sxy

0 d (mn� 1)for d =0 arithmetic mean filter

for d=(mn-1)/2 median filter

can be used for multitude noise problem

48



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Adaptive filters

Adaptive local noise reduction filter

Adaptive median filter

49



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Adaptive filters

Adaptive local noise reduction filter

f̂(x, y) = g(x, y)� �

2n

�

2L

[g(x, y)�m

L

]

�

2n

: noise variance

�

2L

: local variance in S

xy

m

L

: local mean of the pixels in S

xy

Local variance >> noise variance , it preserves the edge information

Two variance almost equal , the output will be arithmetic mean value, thus the edges are not blurred.

50



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Adaptive filtersAdaptive median filter

stage A : A1 = g

med

� g

min

, A2 = g

med

� g

max

If A1 > 0 and A2 < 0, go to stage B

Else increase the window size

If window size S

max

repeat stage A

Else output g

med

stage B : B1 = g

xy

� g

min

, B2 = g

xy

� g

maz

If B1 > 0 and B2 < 0, output g

xy

Else output g

med

g

min

, g

max

are min and max gray level value in S

xy

g

med

is median of gray levels in S

xy

g

xy

is gray level a coordinates (x, y)

S

max

is maximum allowed size of S

xy

Notations:

51



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

Estimation of degradation function:

Now in the case H≠1, we have to estimate the degradation function before doing any restoration.

DEGRADATION FUNCTION H +f(x,y)

n(x,y)

g(x,y)

52



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION


Three ways to do it:

1.Observation

2.Experimentation

3.Mathematical modeling

53



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



ObservationGathering information from the image itself.

e.g: the illustrated image has been undergone blurring

select one part with strong signal content (so the noise is ignored)

construct an unburied image of the same size and characteristics

Hs(u, v) =Gs(u, v)

F̂s(u, v)

54



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



ExperimentationTake images similar to the degraded g(x,y)

Vary the system setting until output images are degraded as closely as possible to g(x,y)

Obtain the impulse response g(x,y) of the system using the same settings

H(u, v) =Gi(u, v)

A

55



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Mathematical modeling

Getting inspired by basic principles to derive mathematical models

Use environmental and physical characteristics

56



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION



Mathematical modeling

e.g: turbulence

57



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

58



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

59

Morphological processing:

it is used to extract image components for representation and description of region shape.

Mathematical morphology:

Based on set theory

Extract image component

Representation and description of region shape



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

60


Main topics,

Set theory

Dilation and erosion

Opening and closing

Basic morphological algorithms



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

61


Main topics,

Set theory

8>>><

>>>:

let A be a set 2 Z2

a = (a1, a2) is an element of A. a 2 A

a is not an element of A. a /2 A

Null set ; (empty)



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

62


Main topics,

Set theory8>>><

>>>:

A is a subset of B : A ✓ B

Union C = A [B

Intersection C = A \B

Mutually exclusive A \B = ;



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

63


Main topics,

Set theory (From graphical point)



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

64


Main topics,

Set theory (From graphical point)

Ac = {w/w /2 A} A�B = {w/w 2 A,w /2 B}



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

65


Main topics,

Set theory (Functionally operations, and, or, not)

p qp and q

p.qp or q p+q Not p

0 0 0 0 10 1 0 1 11 0 0 1 01 1 1 1 0



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

66


Main topics,

Set theory (Functionally operations, and, or, not)



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

67


Main topics,

Set theory

Translation Reflection

B̂ = {d/d = �b, for b 2 B}(A)z = {c/c = a+ z, for a 2 A}



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

68


Main topics,

Dilation A�B = {z/(B̂)z \A 6= ;}



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

69


Main topics,

Dilation Application

z: displacement

B:structuring element



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

70


Main topics,

Erosion A B = {z/(B̂)z ✓ A}



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

71


Main topics,

Erosion A B = {z/(B̂)z ✓ A}



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

72


Main topics,

Erosion Application: delete irrelevant details

original image

Squares of size 1,3,5,7,9,15 pels

erosion

Erode with 13x13 square

dilation



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

73


Main topics,

Erosion Application: boundary extraction

1.Erode A

2.A- erode(A)

b(a) = A� (A B)



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

74


Main topics,

Erosion Application: boundary extraction



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

75


Main topics,

Openingdilation = expand image

erosion= shrink image

erosion + dilation ?

Opening = erosion + dilation

A �B = (A B)�B



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

76


Main topics,

Opening and closing Smooth the contour of an image, breaks narrow isthmuses, eliminates thin protrusions



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

77


Main topics,

Closingdilation = expand image

erosion= shrink image

dilation + erosion ?

Closing = dilation + erosion

A •B = (A�B) B



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

78


Main topics,

Closing Smooth the object contour, fuse narrow breaks and long thin gulfs, eliminate small holes, and fill in gaps



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

79


Main topics,

Opening and closing PROPERTIES

Opening:8><

>:

1. A �B is a subset (subimage) of A open

2. If C is a subset of D, then C �D is a subset of D �B3. (A �B) �B = A �B open

Closing:8><

>:

1. A is a subset (subimage) of A •B close

2. If C is a subset of D, then C •B is a subset of D •B3. (A •B) •B = A •B close



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

80

Noisy image

opening Remove outer noise

Remove inner noise

closing



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

81


Main topics,


Extract image components which are useful for representation and description of shape

Boundary extraction

• Region filling • Extract of connected components • Convex hull • Thinning • Thickening • Skeleton • Pruning



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

82


Main topics,


Region filling

philosophy: place a point inside the region and dilate it iteratively

Xo

= p

Xi = (Xi�1 �B) \Ac,

i = 1, 2, 3... till Xk = Xk�1

Limit the growth



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

83


Main topics,


Region filling



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

84


Main topics,


Region filling



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

85


Main topics,


Extraction of connected componentsphilosophy: start from a point in the connected component, and dilate it iteratively.

Xo

= p

Xi

= (Xi�1 �B) \A,

i = 1, 2, 3... till Xk

= Xk�1

IMAGE ANALYSIS4.

86



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

87



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

88

Segmentation:

Image segmentation refers to the decomposition of a scene into different components.

This process facilitate object detection and recognition



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

89

Segmentation:

Overview of image segmentation techniques:

Edge-based

Color-based

Texture-based

Disparity-based

Motion-based



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

90

Segmentation:


Edge-based

include many methods such as

Sobel, Canny, Prewitt, Roberts, and fuzzy logic methods.

EDGE DETECTION SEGMENTATION BY BOUNDARY DETECTION

CLASSIFICATION AND ANALYSIS

http://se.mathworks.com/products/fuzzy-logic/



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

91

Segmentation:


Color-based Segmenting based on the color representation or color space:

Device dependent: RGB

Device independent: L*a*b*



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

92

Segmentation:


Texture-basedThe idea: texture features extracted from the image tiles and performs a coarse image segmentation based on local texture gradient.



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

93

Segmentation:


Disparity-based

Disparity: is the distance between corresponding points when the two images are superimposed



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

94

Segmentation:


Motion-based

Motion-based segmentation of images refers to partitioning an image into regions of homogenous 2D (apparent) motion.



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

95



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

96

Object detection and recognition:

Template matching

Color based

Shape based

etc.



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

97


Template matching

Color based

Shape based

etc.



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

98


Template matching

Color based

Shape based

etc.



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

99


Template matching

Color based

Shape based

etc.



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

100

IMAGES COMPRESSION5.

101



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

source: http://planbwebsitedesign.com/essential-website-image-basics.html

102

http://planbwebsitedesign.com/essential-website-image-basics.html



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

103

Image compression

It is the process of coding which will reduce the total number of bits needed to represent certain information.

Compression ration:

Rc = B0/B1

B0 is the number of bits before compression

B1 is the number of bits after compression



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

104

Image compression

General process of image coding algorithm

INPUT IMAGE

REDUCE CORRELATION BETWEEN PIXELS QUANTIZATION

ENTROPY CODING

BIT STREAM



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

105

Take Home Quiz:

Read the following article about Compression using Huffman coding

http://paper.ijcsns.org/07_book/201005/20100520.pdf

After reading the article, your task is to implement a simple example of Huffman encoding and decoding algorithm. (deadline 16th, May 2016)

http://paper.ijcsns.org/07_book/201005/20100520.pdf

COLOR IMAGES PROCESSING6.

106



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

107



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

108

Color:

Adv: can help to easily identify and extract objects

Thousands of colors vs 24 gray levels

Spectrum:



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

109

Color models:

RGB

CMY

YIQ

HSL/HSV

HSI



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

110

Color models:

RGB

CMYK

YIQ

HSL/HSV

HSI

Additive color model in which red, green, and blue are added together in different ways to reproduce a broad array of colors



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

111

Color models:

RGB

CMYK

YIQ

HSL/HSV

HSI

Is a subtraction color model from RGB used in color printing. CMYK refers to the four inks used in some color printing: cyan, magenta, yellow, and key (black).

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

BGR

YMC

111



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

112

Color models:

RGB

CMYK

YIQ

HSL/HSV

HSI

Is the color space used by NTSC color TV system, mainly used in America and Japan.Y = luma information

I = in-phase

Q = quadrature and it is the components used in quadrature amplitude modulation

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

×

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−

−−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

BGR

QIY

311.0523.0212.0321.0275.0596.0114.0587.0299.0

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

×

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−

−−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

QIY

BGR

705.1108.11647.0272.01620.0956.01

RGB 2 YIQYIQ 2 RGB



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

113

Color models:

RGB

CMYK

YIQ

HSL / HSV

HSI

HSL and HSV are the most common cylindrical-coordinates representations of points in an RGB color model.

H = hue

S = Saturation

L = Lightness

V = Value



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

114

Color models:

RGB

CMYK

YIQ

HSL / HSV

HSI


H = hue

S = Saturation

I = Intensity

More common and used in computer vision applications

1,,,0 where),(31

≤≤++= BGRIBGRI

002

1 if },))(()(

)]()[(21

{cos bgBGBRGR

BRGRH >

−−+−

−+−= −

00 if ,360 bgHH <−= !

}),,(min{31 BGRBGR

S ×++

−=

IBbIGg / ,/ where 00 ==

RGB 2 HSI



IMAGE ACQUISITION

IMAGE ENHANCEMENT

IMAGE RESTORATION


SEGMENTATION

OBJECT RECOGNITION


Problematic


IMAGE COMPRESSION

115

Color models:

RGB

CMYK

YIQ

HSL / HSV

HSI


H = hue

S = Saturation

I = Intensity

More common and used in computer vision applications

HSI 2 RGB BRGHHSR

SB

−−=

−+=

−=

1

])60cos(

cos1[

31

)1(31

!

!! 1200 assume ≤≤ H

1 scientific data computing mtat.08.042 lecture 4 … · 2016. 5. 2. · lecture 4 image processing...

Documents