Filters

Face Recognition and Biometric Systems 2005/2006

Filters


Plan of presentation

Review of available filters Filter application in various parts of automatic face recognition system Further research


Filter grouping

One pixel operations Pixel area operations

Image histogram operations Image rotation & scaling Complex techniques


One pixel operations

Linear function Power function Logarithmic function

Application Contrast improvement Image sharpness enhancement

)],([),( yxIfyxI inout


Linear function

Scaling Dynamic range scaling in a chosen

sections

222255

2255)2(

)2,1(112

12)1(

11

1

),(

rIforsr

srI

rrIforsrr

ssrI

rIforr

sI

yxI

inin

inin

inin

out


Power function

Gamma correction Image after translation still looks

naturally

45,0),(),( whereyxIyxI inout

0

50

100

150

200

250

1 51 101 151 201 251


Logarithmic function

Gray level compression Natural image look Partial lost of image information

1)(max255

1

)ln(*

)1),(ln(),(

,

Ic

b

cb

yxIayxI

yx

inout

0

50

100

150

200

250

1 51 101 151 201 251


One pixel filters - example

Input image

Logarithm

Scaling

Gamma


One pixel filters

Advantages:Improvement of image contrastBetter sharpness

Disadvantages:Too bright pixels Difficulties with optimal parameters selection


Area filters

Lowpass filters Mean filter Gauss Median

Highpass filters Roberts Prewitt Sobel

Laplacian


Lowpass filters

Noise reduction Image smoothing Contour blurring


Mean filter

Linear filter Light image smoothing

111

111

111

9

1

group

inout II9

1


Gauss filter

Filter uses power function Stronger image smoothing in a shorter time

2

22

222

1),(

yx

eyxG

121

242

121

16

1


Median filter

Nonlinear filter Good for noise removal from image without important information elimination


Lowpass filters - example

Input image

Gauss

Mean

Median


Highpass filters

Image sharpness enhancement Contour detection In case of noisy images the errors will multiply


Roberts filter

Gradient method

000

010

100

xR

000

010

001

yR

||||||

|| 22

yx

yx

RRR

RRR

y

I

x

II

,


Prewitt filter

Gradient method

111

000

111

xP

101

101

101

yP

||||||

|| 22

yx

yx

PPP

PPP

y

I

x

II

,


Sobel filter

Gradient method

121

000

121

xS

101

202

101

yS

22||

||||||

yx

yx

ssS

SSS

y

I

x

II

,


Laplacian filter

Method uses second derivative properties

111

181

111

2

2

2

2

,),(y

I

x

IyxL


Highpass filters - example

Input image

Prewitt

Roberts

Sobel


Histogram operations

Stretching Fitting Equalization


Histogram stretching

Image dynamic range enlargement for image contrast & sharpness enhancement

Does not work on images with characteristic histogram

minmax

min),(*)12(),(

yxI

yxI inBout


Histogram equalization

Equal distribution of gray scale levels in input image Contrast enhancement


Histogram equalization

countpixelKwhereKIhIp /)()(

levelsgreynwhereipiDn

i

0

)()(

valueimageorginalzerononfirstD

D

DIDI

in

B

in

ininout

0

0

0 )12(1

)(

Algorithm:


Histogram fitting

Its aim is a transformation of an input histogram so it looks like the given one Image lighting unification


Histogram fitting

Algorithm: Input & output image histogram

calculation (hIn ,hOut ) Histogram normalization

Increment function calculation

countpixelKwhereKIhIp /)()(

levelsgreynwhereipiDn

i

0

)()(


Histogram fitting

Algorithm:


Histogram - exampleInput image

Equalization

Stretching

Fitting


Histogram

Minimization of lighting differences in images from different sources Image sharpness and contrast enhancement


Image Rotation / Scaling


Complex filters - techniques

Kuwahara Canny Unsharp Masking LogAbout GammaAbout


Kuwahra filter

Nonlinear filters Good image smoothing Low contours blurring Algorithm: For each region: Result:

region

insr In

I1

region

srin II 2)(

)()min( rIIr sroutregions


Canny filter

Optimal contour detection Algorithm: Gauss filter Sobel filter Borders direction described as Direction definition Pixel tracking in the direction of borders

and removal of unnecessary pixels Thresholding

)/(tan 1xy SS


Unsharp Masking

Image sharpening Minor noise elimination Algorithm: I(x,y) = Gauss(Iin(x,y)) Ihp(x,y) = Iin(x,y) – I(x,y) Ihp(x,y) = 0 dla Ihp(x,y) < threshold Iout(x,y) = Iin(x,y) + a*Ihp(x,y)


LogAbout method

Contour detection improvement

Highpass filter

Logarithmicfilter


HistAbout method

Contour detection enhancement

Histogram stretching

Gauss

LogAbout


GammaAbout method

Contour detection improvement

Gamma

Gauss

LogAbout


Where use filers?

Input image Detection Normalization


Input image

Problems: Noises

Solution: Gauss filter Median filter


Input image/Detection

Problem: Dark image

Solution: Histogram stretching Gamma correction GammaAbout


Detection

Problem: Contour detection

Solution: Roberts filter Prewitt filter Sobel filter Canny’s method


Shape normalization

Problem: Lack of size unification Solution: Scaling

Problem: Non frontal face Solution: Rotation


Lighting normalization

Problem: Irregular face lightning

Solution: Histogram operations


Filter usage

Image quality enhancement Object detection method efficiency improvement Image normalization Lighting normalization


What further??

Lighting normalization is still an area for research Dark image brightening


Thank You

Filters

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