9
Julia Ebling, Gerik Scheuermann Clifford Convolution and Fourier Transform on Vector Fields Julia Ebling, Gerik Scheuermann University of Leipzig Julia Ebling, Gerik Scheuermann Flow Feature Detection Using Image Processing? Pattern matching intuitive Convolution robust in terms of noise Analysis of filter behaviour Julia Ebling, Gerik Scheuermann Convolution and Fourier Transform on Vector Fields Convolution of each coordinate separately [Granlund, Knutsson 1995] Scalar product in convolution [Heiberg 2001] Clifford Convolution [Ebling, Scheuermann 2003] Clifford Fourier Transform [Ebling, Scheuermann 2004] Julia Ebling, Gerik Scheuermann Overview Clifford Algebra Clifford Convolution Clifford Fourier transform Gabor Filter Future work

Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

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Page 1: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Con

volu

tion

and

Fou

rier

Tra

nsfo

rm o

n V

ecto

r Fi

elds

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Uni

vers

ity o

f Lei

pzig

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Flow

Fea

ture

Det

ectio

n U

sing

Imag

e Pr

oces

sing

?

Pat

tern

mat

chin

g in

tuiti

ve

Con

volu

tion

robu

st in

term

s of n

oise

Ana

lysi

s of f

ilter

beh

avio

ur

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Con

volu

tion

and

Four

ier

Tra

nsfo

rm o

n V

ecto

r Fi

elds

Con

volu

tion

of e

ach

coor

dina

te se

para

tely

[Gra

nlun

d, K

nuts

son

1995

]

Sca

lar p

rodu

ct in

con

volu

tion

[Hei

berg

200

1]

Clif

ford

Con

volu

tion

[Ebl

ing,

Sch

euer

man

n 20

03]

Clif

ford

Fou

rier T

rans

form

[Ebl

ing,

Sch

euer

man

n 2

004]

Julia

Ebl

ing,

Ger

ik S

cheu

erm

annO

verv

iew

Clif

ford

Alg

ebra

Clif

ford

Con

volu

tion

Clif

ford

Fou

rier t

rans

form

Gab

or F

ilter

Futu

re w

ork

Page 2: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Alg

ebra

For

Eucl

idea

n 3D

-spa

ce,

we

use

a 8-

dim

ensi

onal

real

alg

ebra

G3

with

the

vec

tor

basi

s {1

, e 1, e

2, e 3,

e 1e 2, e3e 1, e

2e 3, e1e 2e 3}.

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Alg

ebra

1ej

=e j

j=1,

2,3

e je j

=1

j=1,

2,3

e je k

=­e ke j

j,k=

1,2,

3,j

≠k

Mul

tiplic

atio

n is

bili

near

and

ass

ocia

tive

with

the

rule

s:

Mul

tiplic

atio

n is

not

com

mut

ativ

e!

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Vec

tors

are

des

crib

ed a

s

and

an a

rbitr

ary

mul

tivec

tor c

an b

e de

scrib

ed a

s

with

i=e 1e 2e 3.

v=xe

1ye

2ze

3∈E

3 ⊂G

3

A=

aib

∈G

3,

∈ℝ,a,b

∈E

3

Clif

ford

Alg

ebra

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

In C

liffo

rd a

lgeb

ra, t

he m

ultip

licat

ion

of v

ecto

rs

desc

ribes

the

com

plet

e ge

omet

ric re

latio

n be

twee

n tw

o ve

ctor

s:

Her

e,

is th

e sc

alar

(inn

er) p

rodu

ct b

etw

een

two

vect

ors a

nd

is t

he o

uter

pro

duct

:

ab=a⋅ba∧b

a⋅b

a∧b a⋅b

=∣ a

∣∣ b∣ cos

a;b

∣ a

∧b∣

=∣ a

∣∣ b∣ sin

a;b

Clif

ford

Alg

ebra

Page 3: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Let

be

a m

ultiv

ecto

r fie

ld. A

s dire

ctio

nal

deriv

ativ

e, w

e de

fine

and

as to

tal d

eriv

ativ

e

As i

nteg

ral,

we

defin

e

A br

=lim

0

1 [A

r

b­A

r],

∈ℝ

∂A

r=

∑k=

1

3e kA e

kr

∫ E3Adx

=lim

i∞∑

iAx i

xi

A:E

3G

3Clif

ford

Alg

ebra

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Cur

l and

div

erge

nce

of a

vec

tor v

alue

d fu

nctio

n

ar

e de

fined

as:

divergence

f=

⟨∇,f

⟩=∂f

f∂

2

Clif

ford

Alg

ebra

f

curl

f=

∇∧f=

∂f­

f∂

2

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Con

volu

tion

With

the

int

egra

l, th

e C

liffo

rd c

onvo

lutio

n is

def

ined

as A

com

paris

on w

ith [

Hei

berg

200

1] s

how

s th

at w

e ge

t fo

r a v

ecto

r filt

er h

is s

cala

r con

volu

tion

plus

a b

ivec

tor

part

desc

ribin

g th

e re

lativ

e po

sitio

n in

spac

e.

F∗V

x

=∫ E

3F

yV

x­ydy

F∗V

j,k,l

=

∑s,t,u=

­r

rF

s,t,u

Vj

­s,k­t,l­u

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Inte

rpre

tatio

n

Inst

ead

of th

e co

nvol

utio

n, w

e m

ay lo

ok a

t the

spa

tial

cohe

renc

e:

At e

ach

posi

tion

x, w

e co

mpu

te th

e co

here

nce

betw

een

the

mas

k ce

nter

ed a

t its

orig

in a

nd t

he v

ecto

r fie

ld!

By

the

Clif

ford

pro

duct

, we

get t

he r

elat

ive

geom

etric

po

sitio

n be

twee

n m

ask

and

vect

or fi

eld.

F×P

x

=∫ E

3F

yP

xydy

F×P

j,k,l

=

∑s,t,u=

­r

rF

s,t,u

Pj

s,kt,lu

Page 4: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Vec

tor

Filte

r

As

vect

or

valu

ed

mas

ks fo

r pa

ttern

m

atch

ing

we

can

use

typi

cal

patte

rn su

ch

as ro

tatio

n or

co

nver

genc

e.

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Vec

tor

Filte

r

Her

e ar

e so

me

vect

or v

alue

d fil

ter i

n 3D

:

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Patt

ern

mat

chin

gC

liffo

rd C

onvo

lutio

n:A

ppro

xim

atio

n of

rota

tion

betw

een

loca

l st

ruct

ure

in fi

eld

and

mas

kR

otat

e m

ask

to a

lign

field

and

mas

kC

ompu

te sc

alar

con

volu

tion

for s

imila

rity

➔ R

otat

ion

inva

rian

t pat

tern

mat

chin

g

App

roxi

mat

ion

not g

ood

enou

gh:

Use

3 (2

D) /

6 (3

D) m

ask

dire

ctio

ns

to c

ompu

te th

e lo

cal d

irect

ion

2D

3D

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Patt

ern

mat

chin

g

2D3D

Com

puta

tion

of lo

cal d

irec

tion:

2D: a

ppro

xim

atio

n w

ith sm

alle

st a

ngle

3D: w

eigh

ted

aver

agin

g of

app

roxi

mat

ions

with

posi

tive

scal

ar

Page 5: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Gas

furn

ace

cham

ber

Gas

furn

ace

cham

ber.

Patte

rn

mat

chin

g w

ith d

iffer

ent m

ask

size

s: 3◊3◊3

(red

), 5◊

5◊5

(yel

low

), 8◊

8◊8

(gre

en)

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Flat

tene

d su

rfac

e of

del

ta w

ing

Del

ta w

ing

at 0

.2 m

ach

vel

ocity

, a

ngle

of a

ttack

: 25∞

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Fou

rier

tran

sfor

m

{1,I 3=e

1e 2e 3 }

isom

orph

to c

ompl

ex n

umbe

rs

I 3 co

mm

utes

with

eve

ry m

ultiv

ecto

r

Use

I 3 in

stea

d of

i in

Fou

rier K

erne

l

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Fou

rier

Tra

nsfo

rm in

3D

:

F{f

}u

=∫fx

e­2I 3

⟨x,u

⟩ dx

F{f

}u

=∫fx

e2I 3

⟨x,u

⟩ dx

Page 6: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Fou

rier

Tra

nsfo

rm in

3D

:

Shift

The

orem

:

Con

volu

tion:

Der

ivat

ion:

F{x

­x'

}u

=F

{f}u

2I 3

⟨x',u⟩

F{h

∗f}

u=F

{h}u

F{f

}u

F{∇f}

u=

2I 3uF

{f}u

F{f }

u=

­4

2 u2F

{f}u

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Fou

rier

Tra

nsfo

rm in

3D

:

Bas

icly

4 c

ompl

ex F

ourie

r tra

nsfo

rms o

f:

1 -

e1e 2e 3

e 1 -

e2e 3

e 2

- e 3e 1

e 3

- e 1e 2,

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

{1,I 2=e

1e 2 }

isom

orph

to c

ompl

ex n

umbe

rs

I 2 co

mm

utes

with

eve

ry sp

inor

I 2antic

omm

utes

with

vec

tor

U

se I 2

inst

ead

of i

in F

ourie

r Ker

nel

Th

eore

ms a

bit

mor

e co

mpl

icat

ed

Clif

ford

Fou

rier

Tra

nsfo

rm in

2D

:

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Clif

ford

Fou

rier

Tra

nsfo

rm in

2D

:

Bas

icly

2 c

ompl

ex F

ourie

r tra

nsfo

rms o

f:

1 -

e1e 2

e 1 -

e2

as

a 1e 1+ a 2e 2 =

e1 ( a

11 +

a2e 1e 2 )

can

be

unde

rsto

od a

s a c

ompl

ex n

umbe

r

Page 7: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Pars

eval

s the

orem

Sam

plin

g th

eore

m

Dis

cret

izat

ion

Fast

tran

sfor

m

Clif

ford

Fou

rier

Tra

nsfo

rm:

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Turb

ulen

t sw

irlin

g je

t en

teri

ng fl

uid

at r

est

Res

olut

ion:

256

*128

Col

or c

odin

g of

the

abso

lute

val

ues o

f the

(mul

ti) v

ecto

rs

Top:

or

igin

al v

ecto

r fie

ld

Bot

tom

: fa

st C

FT, 2

D v

ecto

rs a

re

conv

erte

d to

vec

tors

(3D

vec

tors

con

vert

to

vect

or +

biv

ecto

r)

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Vec

tor

Filte

r in

Fre

quen

cy D

omai

n

Rot

atio

n,

Div

erge

nce,

Sa

ddle

Poi

nts:

Diff

er o

nly

in

phas

e, n

ot in

am

plitu

de!

Julia

Ebl

ing,

Ger

ik S

cheu

erm

annGab

or F

ilter

Shor

t tim

e or

win

dow

ed F

ourie

r tra

nsfo

rm

Opt

imal

ly lo

caliz

ed in

bot

h sp

atia

l and

Fou

rier

dom

ain

Gab

or e

xpan

sion

, wav

elet

s, fil

ter b

anks

Page 8: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

annGab

or F

ilter

scal

ar G

abor

filte

r in

spat

ial d

omai

n:

➔ m

ultiv

ecto

r val

ued

Gab

or fi

lter i

n sp

atia

l dom

ain:

hx

=gx

∗e­

2i⟨x,U

hx

=gx

∗e­

2I k

⟨x,U

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Con

clus

ion:

Pro

/Con

tra

Clif

ford

Con

volu

tion:

+ un

ifyin

g no

tatio

n fo

r sca

lar /

vec

tor f

ield

s+

vect

or fi

elds

: sim

ilarit

y an

d ge

omet

ric p

ositi

on

Patte

rn m

atch

ing:

+

robu

st in

term

s of n

oise

+ ro

tatio

n in

varia

nt+

appl

icab

le to

irre

gula

r grid

s- s

low

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Con

clus

ion:

Pro

/Con

tra

Four

ier t

rans

form

:+

conv

olut

ion

thor

em, .

..+

mat

hem

atic

al b

asis

for a

naly

sis o

f filt

er+

acce

lera

tion

of c

onvo

lutio

n vi

a FF

Ts-

irreg

ular

grid

s

Gab

or F

ilter

:+

inhe

rent

mul

tisca

le a

ppro

ach

- di

rect

app

roac

h ga

ve n

o bi

g ad

vant

ages

for

m

atch

ing

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Futu

re W

ork

Irre

gula

r Grid

s

Ana

lysi

s of i

nter

pola

tion,

smoo

thin

g, sa

mpl

ing,

de

rivat

ion

and

the

indu

ced

erro

rs

➔Fi

lter D

esig

n

Scal

e sp

ace

cons

ider

atio

n / h

iera

rchi

cal f

eatu

res

Page 9: Pattern matching intuitive Convolution robust in terms of noise …vis.computer.org/vis2004/dvd/tutorial/tut_7/4_ebling.pdf · 2005. 5. 14. · University of Leipzig Data Sets: Wolfgang

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Ack

now

ledg

men

ts

FAnT

oM so

ftwar

e de

velo

pmen

t tea

m(T

om B

obac

h, C

hris

toph

Gar

th, D

avid

Gru

ys, K

ai H

erge

nrˆt

her,

Nik

olai

Ivle

v, M

ax L

angb

ein,

Mar

tin ÷

hler

, Mic

hael

Sch

lem

mer

, X

avie

r Tric

oche

, Tho

mas

Wis

chgo

ll)

Com

pute

rgra

phic

s Gro

up a

t TU

Kai

sers

laut

ern

and

Uni

vers

ity o

f Lei

pzig

Dat

a Se

ts:

Wol

fgan

g K

ollm

ann,

MA

E D

epar

tmen

t, U

C D

avis

Mar

kus R

¸tte

n, D

LR G

ˆttin

gen

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Four

ier

Tra

nsfo

rm o

f Sec

ond-

Ord

er T

enso

r Fi

elds

F{f

}u

=∫fx

e­2iI

⟨x,u

⟩ dx

f∗v

x=

∫ E3fyvx­ydy

Con

volu

tion

usin

g m

atrix

mul

tiplic

atio

n

Four

ier t

rans

form

➔ C

onvo

lutio

n th

eore

m:

F{h

∗f }

u=F

{h}u

F{f

}u

Julia

Ebl

ing,

Ger

ik S

cheu

erm

ann

Four

ier

Tra

nsfo

rm o

f Arb

itrar

y O

rder

Ten

sor

Fiel

ds

F{f

}u

=∫fx

e­2i⟨x,u⟩dx

f∗v

x=

∫ E3fyvx­ydy

Con

volu

tion

usin

g te

nsor

pro

duct

Four

ier t

rans

form

➔ C

onvo

lutio

n th

eore

m:

F{h

∗f}

u=F

{h}u

F{f

}u