ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA...
Transcript of ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA...
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DIG
ITA
LIn
stitu
te o
f Inf
orm
atio
n an
d C
omm
unic
atio
nT
echn
olog
ies
Com
pute
r V
isio
n A
lgor
ithm
s fo
r A
utom
atin
g H
D P
ost-
Pro
duct
ion
Han
nes
Fas
sold
, Jak
ub R
osne
r20
10-0
9-22
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2
Ove
rvie
w�
Har
ris/K
LT fe
atur
e po
int d
etec
tion
�K
LT fe
atur
epo
int t
rack
ing
�A
pplic
atio
n�
Rea
l-tim
eH
D s
tabi
lizat
ion
�Im
age
war
ping
�Im
age
inpa
intin
g�
App
licat
ion
�R
e-T
imin
g (‚T
ime-
Str
etch
ing‘
)
�R
esto
ratio
nof
dam
aged
/ mis
sing
fram
es
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3
Fea
ture
poi
nt d
etec
tion
Intr
oduc
tion
�F
ind
‘relia
ble’
feat
ure
poin
ts in
imag
e�
Usa
ge�
Cam
era
calib
ratio
n�
Tra
ckin
g�
…
�R
elia
ble
feat
ure
poin
ts h
ave
suffi
cien
t str
uctu
re
in th
eir
loca
l nei
ghbo
rhoo
d�
E.g
. poi
nt w
ithin
hom
ogen
eous
area
notr
elia
ble
�R
elia
ble
feat
ure
poin
tsty
pica
llylo
okco
rner
-like
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4
Fea
ture
poi
nt d
etec
tion
Mea
sure
s�
Str
uctu
rem
atrix
G�
2 x
2 m
atrix
Gen
code
sst
ruct
ure
info
rmat
ion
for
an r
ecta
ngul
arar
eaW
(p)
arou
nda
poin
t p
�G
radi
ent i
mag
e →
Cen
tral
Diff
eren
ce, S
obel
orS
harr
oper
ator
�C
orne
rnes
sm
easu
res
�H
arri
sm
easu
re: λ
= d
et(G
) –
k*
trac
e(G
)^2
�K
LT
mea
sure
: λ=
min
imum
eige
nval
ueof
G
�λ
smal
lor
zero
→ho
mog
eneo
usim
age
area
�λ
big
→co
rner
, ric
hly
text
ured
area
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5
Fea
ture
poi
ntde
tect
ion
Alg
orith
m�
As
in O
penC
Vro
utin
e‚c
vGoo
dFea
ture
sToT
rack
‘�
Alg
orith
mst
eps
1.C
alcu
late
corn
erne
ssλ
for
all p
ixel
s2.
Cal
cula
tem
axim
umco
rner
nessλ
max
in im
age
3.D
isca
rdal
l pix
els
whi
chλ
smal
ler
than
a fr
actio
nof
λm
ax(e
.g. <
5%
)
4.N
on-m
axim
asu
ppre
ssio
n(d
isca
rd‚w
eak‘
loca
lmax
ima)
5.M
inim
um d
ista
nce
enfo
rcem
ent
�M
inim
um d
ista
nce
enfo
rcem
ent
�E
nsur
esth
atev
ery
feat
ure
poin
t has
a c
erta
inm
inim
umdi
stan
ce to
all
othe
rpo
ints
�A
void
scl
umpi
ngof
mos
tfea
ture
poin
tsin
ric
hly
text
ured
imag
e re
gion
s
�S
ome
issu
esw
ithit
whi
chfo
rce
usto
do
iton
CP
U (
mor
ela
ter)
…
GP
U
CP
U
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6
Fea
ture
poi
nt d
etec
tion
Ste
ps 1
-4, C
UD
A im
plem
enta
tion
�A
ll ke
rnel
sm
ake
exte
nsiv
e us
age
of s
hare
dm
emor
y�
Cor
nern
ess
calc
ulat
ion
�T
hree
kern
els
for
conv
olut
ion,
str
uctu
rem
atrix
, cor
nern
ess
(KLT
form
ula)
�D
eter
min
em
axim
umco
rner
nessλ
max
�Is
a re
duct
ion
oper
atio
n→
CU
DP
P li
brar
y
�D
isca
rdfe
atur
epo
ints
with
low
corn
erne
ss�
Set
a ‚d
isca
rdfla
g‘fo
rea
chpi
xelt
o be
disc
arde
d
�N
on-m
axim
asu
ppre
ssio
n�
Ker
neli
sva
riatio
nof
dila
teop
erat
or
�(B
efor
e5)
Tra
nsfe
r no
n-di
scar
ded
pixe
lsto
CP
U�
Bef
ore
tran
sfer
, all
disc
arde
dpi
xels
are
filte
red
out b
ya
com
pact
ion
oper
atio
n→
CU
DP
P li
brar
y
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7
Fea
ture
poi
nt d
etec
tion
Ste
p 5
�M
inim
um d
ista
nce
enfo
rcem
ent
�G
iven
: ‚ca
ndid
ate
list‘
(all
pixe
lsw
hich
have
n‘tb
een
disc
arde
d)�
Itera
teth
roug
hlis
t, st
artin
gw
ithca
ndid
ates
with
high
estc
orne
rnes
s, a
nd
add
them
to o
utpu
tlis
t�
Bef
ore
addi
nga
cand
idat
e, it
sdi
stan
ce to
all
poin
tsal
read
yin
out
putl
ist
isch
ecke
d
�Is
sues
�P
roce
ssis
inhe
rent
lyse
rial→
forc
esus
to d
o on
CP
U�
Ope
nCV
impl
emen
tatio
nno
teffi
cien
tfor
seve
ralt
hous
and
poin
ts�
Dev
elop
edal
tern
ativ
e m
etho
d�
Prin
cipl
e: W
hen
addi
nga
cand
idat
e, t
heci
rcul
arar
eaar
ound
itis
mar
ked
as ‚o
ccup
ied‘
.�
Line
ar c
ompl
exity
�A
utom
atic
sw
itchi
ngbe
twee
nO
penC
Van
d al
tern
ativ
e m
etho
d
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8
Fea
ture
poi
ntde
tect
ion
Res
ults
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9
Fea
ture
poi
ntde
tect
ion
Res
ults
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10
Fea
ture
poi
nt d
etec
tion
Run
time
com
paris
on�
GP
U im
pl.:
CU
DA
, GT
X 2
80
�C
PU
impl
.: O
penC
V(u
sing
IPP
), 2
.4 G
hzX
eon
Qua
d-C
ore
�W
indo
wsi
ze=
5 x
5, M
axim
um #
of f
eatu
res
= 1
0000
Run
time
for
the
step
s1
–4
(fea
ture
poin
t det
ectio
nw
ithou
tmin
imum
dist
ance
enf
orce
men
t)R
untim
efo
rst
ep5
(min
imum
dist
ance
enf
orce
men
t)
![Page 11: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/11.jpg)
11
Fea
ture
poi
nt tr
acki
ngIn
trod
uctio
n�
Fea
ture
poi
nt tr
acki
ng�
Giv
ena
spar
sese
tof f
eatu
repo
ints
in c
urre
ntim
age
I(e.
g. fo
und
byfe
atur
epo
int d
etec
tion)
, fin
d th
eir
posi
tion
in s
ubse
quen
tim
age
J
�Im
port
antl
ow-le
velt
ask
in c
ompu
ter
visi
on�
Use
dfo
rob
ject
trac
king
, cam
era
mot
ion
estim
atio
n,
stru
ctur
efr
omm
otio
n, …
�K
LT a
lgor
ithm
(Kan
ade,
Luc
as, T
omas
i)�
Ver
ypo
pula
rm
etho
d�
Rea
sona
bly
fast
, ful
lyau
tom
atic
, suf
ficie
ntqu
ality
�F
or e
ach
fram
eI i
n se
quen
ce�
Det
ectn
ewfe
atur
esin
Ian
d ad
dth
emto
alre
ady
exis
ting
ones
�T
rack
all
feat
ures
from
I to
subs
eque
ntim
age
J
![Page 12: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/12.jpg)
12
Fea
ture
poi
nt tr
acki
ngA
lgor
ithm
prin
cipl
e�
Dis
sim
ilarit
yfu
nctio
n�
p…
poin
t, v
.. m
otio
nve
ctor
,W
(p)
.. n
x n
win
dow
cent
ered
at p
�F
or e
ach
poin
t p, f
ind
mot
ion
vth
atm
inim
izesε(v
)�
Min
imiz
atio
nof
ε(v
)�
Gra
dien
t des
cent
met
hod
(iter
ativ
e m
etho
d, G
auss
-New
ton
type
)�
Gra
dien
t des
cent
met
hods
need
‚goo
d‘in
itial
valu
ev 0
�C
reat
em
ulti-
reso
lutio
nim
age
pyra
mid
�D
o m
inim
izat
ion
on e
ach
leve
lof p
yram
id
�S
olut
ion
of le
velm
+ 1
isus
edas
initi
aliz
atio
nfo
rle
velm
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13
Fea
ture
poi
nt tr
acki
ngA
lgor
ithm
�P
seud
o-C
ode
�F
or o
neP
yram
id L
evel
, for
one
poin
t�
Typ
ical
ly: W
(p)
= 5
x 5
pix
el, m
axIte
r=
10,
eps
= 0
.03
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14
Fea
ture
poi
nt tr
acki
ngC
UD
A im
plem
enta
tion
�G
auss
ian
imag
e py
ram
id�
Con
volu
tion
+ s
ubsa
mpl
ing
�F
eatu
re p
oint
trac
king
(key
issu
es)
�O
ne k
erne
lcal
lfor
each
pyra
mid
leve
l
�O
ne th
read
= o
nepo
int
�G
PU
und
er-u
tiliz
atio
nif
# po
ints
isto
osm
all(
e.g.
som
ehu
ndre
dpo
ints
)
�R
educ
e#
of te
xtur
efe
tche
s, e
spec
ially
in in
ner
loop
�E
ach
thre
adne
eds
lot o
f sha
red
mem
ory
�E
spec
ially
for
bigg
erw
indo
wsi
zes
(9x9
, 11x
11, .
.) th
isle
ads
tolo
wm
ultip
roce
ssor
occu
panc
y
�D
iffic
ultt
o fin
d be
st c
ompr
omis
e(t
hrea
dbl
ock
size
, # r
egis
ters
per
thre
ads…
)�
Lot o
f exp
erim
enta
tion
nece
ssar
y, n
eed
to im
plem
entd
iffer
ent v
aria
nts
![Page 15: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/15.jpg)
15
Fea
ture
poi
nt tr
acki
ngR
esul
ts
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16
Fea
ture
poi
nt tr
acki
ng
Run
time
com
paris
on�
GP
U im
pl.:
CU
DA
, GT
X 2
80
�C
PU
impl
.: O
penC
V(u
sing
IPP
), 2
.4 G
hzX
eon
Qua
d-C
ore
�F
ullH
D(1
920
x 10
80),
win
dow
size
= 5
x 5
, #le
vels
= 6
, max
Iter
= 1
0, e
ps=
0.0
3
![Page 17: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/17.jpg)
17
Fea
ture
poi
nt tr
acki
ngA
pplic
atio
n: S
tabi
lizat
ion
�P
robl
em�
Ann
oyin
gfil
m e
xper
ienc
edu
eto
imag
e ‚v
ibra
tion‘
�P
ossi
ble
reas
ons
�sh
aky
cam
era
�in
stab
ility
in fi
lm tr
ansp
ortd
urin
gfil
m s
cann
ing
(‚wor
nou
t per
fora
tions
‘)
�W
hatw
ew
ant
�R
educ
e/re
mov
eth
ese
vibr
atio
ns
�…
butl
eave
inte
nded
(typ
ical
ly‚s
moo
th‘)
cam
era
mot
ion
inta
ct
![Page 18: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/18.jpg)
18
Fea
ture
poi
nt tr
acki
ngA
pplic
atio
n: R
ealti
me
HD
Sta
biliz
atio
n�
Alg
orith
mou
tline
�T
rack
feat
ure
poin
tsth
roug
hout
sequ
ence
�R
obus
tlyes
timat
e‚g
loba
l‘m
otio
nbe
twee
nco
nsec
utiv
efr
ames
�2-
para
met
er tr
ansl
atio
nalm
odel
(dx,
dy)
�H
ighe
r-pa
ram
eter
mod
elpo
ssib
le(e
.g. a
ffine
mod
el)
�F
ilter
sig
nalt
o ge
tam
ount
of c
orre
ctio
n
�W
arp
fram
esw
ithco
rrec
tion
10 0
-10
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19
Fea
ture
poi
nt tr
acki
ngS
tabi
lizat
ion
Dem
o�
Sta
biliz
atio
nw
ith2-
para
met
er tr
ansl
atio
nalm
odel
�W
orks
rea
ltim
e(>
25
fps)
for
Ful
l HD
res
olut
ion
�G
PU
: GT
X 2
85, C
PU
: Qua
dCor
eX
eon
�[V
ideo
_Ste
yrer
_gas
se]
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20
Imag
e w
arpi
ngIn
trod
uctio
n�
Giv
enan
sou
rce
imag
e Ia
nd a
non
linea
rm
appi
ngM
, ca
lcul
ate
the
map
ped
imag
e M
(I)
�Im
age
war
ping
exam
ples
�R
otat
ion,
Sca
ling,
..
�A
rbitr
ary
mes
hde
form
atio
ns
�M
appi
ngfu
nctio
nM
�T
ypic
ally
defin
edpi
xel-w
ise
![Page 21: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/21.jpg)
21
Imag
e w
arpi
ngA
lgor
ithm
�U
seac
cum
ulat
orim
age
Aan
d w
eigh
tim
age
W�
float
ing-
poin
tor
fixed
-poi
nt
�A
lgor
ithm
�F
or e
ach
sour
cepi
xelp
�D
eter
min
ede
stin
atio
nlo
catio
nds
t= M
(p)
�In
crem
entt
hefo
ursu
rrou
ndin
gpi
xels
in a
ccum
ulat
oran
dw
eigh
tim
age
→‚b
iline
arw
ritin
g‘
�W
arpe
dim
age
M(I
) =
�P
ixel
-wis
edi
visi
on
�P
ixel
s w
ithw
eigh
tzer
oar
em
arke
das
‚hol
es‘
(no
sour
cepi
xelm
appe
dto
them
)
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22
Imag
e w
arpi
ngC
UD
A im
plem
enta
tion
�Is
sues
�A
tom
icop
erat
ions
nece
ssar
yfo
rre
solv
ing
read
-writ
eha
zard
s→
sign
ifica
ntpe
rfor
man
cepe
nalty
for
pre-
Fer
mih
ardw
are
�R
educ
epe
rfor
man
cepe
nalty
�D
eter
min
ea
targ
etre
gion
whe
rem
osto
f thr
eads
of th
ecu
rren
tth
read
bloc
k w
ill li
kely
map
to�
Ass
ume
som
eso
rtof
sm
ooth
ness
in m
appi
ngfu
nctio
nM
�T
arge
t reg
ion
isca
ched
in s
hare
dm
emor
y
�T
hrea
dm
aps
into
targ
etre
gion
→do
sha
red
mem
ory
atom
icop
erat
ion
�T
hrea
ddo
esn‘
tmap
into
targ
etre
gion
→do
glo
balm
emor
yat
omic
oper
atio
n(s
low
er)
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23
Imag
e w
arpi
ngS
ourc
e im
age
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24
Imag
e w
arpi
ngW
arpe
d im
age
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25
Imag
e in
pain
ting
Intr
oduc
tion
�Im
age
inpa
intin
g�
Fill
up u
ndef
ined
regi
ons
in a
n im
age
in th
ebe
st w
ay�
Lot o
f lite
ratu
reab
outi
npai
ntin
gal
gorit
hms
�P
ropa
gate
stru
ctur
e&
text
ure
clev
er in
toho
le
�S
till a
har
dta
sk
�G
oal
�D
evel
opsi
mpl
e an
d fa
st in
pain
ting
algo
rithm
�G
ood
para
lliza
ble
�S
uita
ble
for
hole
soc
curin
gin
war
ped
imag
es�
Thi
n, c
rack
-like
appe
aran
ce
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26
Imag
e in
pain
ting
Alg
orith
m�
App
roac
h�
Use
sac
cum
ulat
orim
age
Aan
d w
eigh
tim
age
W�
Det
erm
ine
seto
f hol
e bo
rder
pixe
ls
�F
or e
ach
bord
erpi
xel
�P
ropa
gate
itsin
tens
ityin
toth
eho
le
alon
ga
fixed
seto
f dire
ctio
ns(e
.g. 1
6)
�B
orde
rpi
xeli
nten
sity
prop
agat
ion
�T
race
the
line
from
bord
erpi
xeli
nto
hole
inte
rior
(Bre
senh
am)
�F
or e
ach
visi
ted
pixe
lpits
valu
ein
Aan
d W
isup
date
d
�In
pain
ted
imag
e I h
olef
illed
=
bcu
rr
gd
pA
pA
1)
()
(+
=cu
rrd
pW
pW
1)
()
(+
=
Bor
der
pixe
lpro
paga
tion
alon
gse
vera
ldire
ctio
ns
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27
Imag
e in
pain
ting
CU
DA
impl
emen
tatio
n�
Key
issu
es�
One
thre
ad=
one
bord
erpi
xel
�O
ne k
erne
lcal
lper
dire
ctio
n�
All
thre
ads
trac
ein
tosa
me
dire
ctio
ns
�A
tom
icop
erat
ions
no
tus
ed�
Spe
edre
ason
s
�F
loat
ato
mic
oper
atio
nsno
tsup
port
edfo
rpr
e-F
erm
iGP
Us
�R
/W H
azar
dsca
nno
tocc
urfo
r8
mai
ndi
rect
ions
�R
/W H
azar
dsca
noc
cur
(ver
yse
ldom
ly)
for
8 se
cond
ary
dire
ctio
ns→
Indu
ces
negl
igib
ledi
ffere
nces
betw
een
CP
U &
GP
U in
pain
ting
resu
lt
�W
arp
dive
rgen
ce�
Due
to d
iffer
ent p
aths
the
thre
ads
of a
war
par
etr
acin
g
�P
ossi
ble
impr
ovem
ent:
Gro
up th
read
id‘s
byso
me
spat
ialr
elat
ions
hip
![Page 28: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/28.jpg)
28
Imag
e w
arpi
ng &
inpa
intin
gR
untim
e co
mpa
rison
�G
PU
impl
.: C
UD
A, G
TX
285
�C
PU
impl
.: O
wn
optim
ized
impl
., o
ne
CP
U-t
hrea
d(b
utus
esm
ulti-
thre
aded
IPP
-fun
ctio
ns),
Inte
l Xeo
nQ
uad-
Cor
e3.
0 G
hz
�A
vera
geru
ntim
eov
erse
quen
ce, w
arpi
ngfu
nctio
ns=
mot
ion
field
s,
~ 1
.3 %
of w
arpe
dim
ages
to b
ein
pain
ted
![Page 29: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/29.jpg)
29
Imag
e in
pain
ting
Res
ults
![Page 30: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/30.jpg)
30
Imag
e in
pain
ting
& w
arpi
ngA
pplic
atio
n: T
ime-
Str
etch
ing
�T
ime-
Str
etch
ing
effe
ct�
Inse
rtsy
nthe
tical
lyge
nera
ted
fram
esin
vid
eose
quen
ceto
ach
ieve
slow
-m
otio
nef
fect
�G
ener
ate
synt
hetic
fram
ebe
twee
nim
age
I 1an
d I 2
�C
alcu
late
pixe
l-wis
em
otio
n(o
ptic
alflo
w)
betw
een
I 1an
d I 2
�F
ast G
PU
met
hods
avai
labl
e
�S
cale
mot
ion
acco
rdin
gto
des
ired
timep
oint
�W
arp
I 1w
ithsc
aled
mot
ion
�F
illho
les
in w
arpe
dim
age
![Page 31: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/31.jpg)
31
Imag
e in
pain
ting
& w
arpi
ngD
emo:
Tim
e-S
tret
chin
g�
Str
etch
ing
Fac
tor
2.0
�[D
emoV
ideo
‚TU
Mun
ich
pede
stria
nar
ea]
![Page 32: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/32.jpg)
32
Imag
e in
pain
ting
& w
arpi
ngA
pplic
atio
n: R
esto
re d
amag
ed fr
ames
�U
sene
ighb
orfr
ames
to g
ener
ate
‚repl
acem
ent‘
for
dam
aged
fram
e
![Page 33: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/33.jpg)
33
Ack
now
ledg
men
ts�
Sile
sian
Uni
vers
ity, P
olan
d�
Jaku
b R
osne
r
�JO
AN
NE
UM
RE
SE
AR
CH
, Aus
tria
�F
loria
n P
utz,
Her
man
n F
uern
trat
t, W
erne
r B
aile
r, P
eter
S
chal
laue
r, G
eorg
Tha
lling
er
�W
ork
was
sup
port
ed b
y E
urop
ean
Uni
on
proj
ects
�20
20 3
D M
edia
(ht
tp://
ww
w.2
0203
dmed
ia.e
u)
�F
asci
natE
(http
://w
ww
.fasc
inat
e-pr
ojec
t.com
)
�P
rest
oPR
IME
(http
://w
ww
.pre
stop
rime.
eu)
![Page 34: ies DIGITAL Computer Vision Algorithms for Automating HD ... · Feature point tracking CUDA implementation Gaussian image pyramid Convolution + subsampling Feature point tracking](https://reader034.fdocuments.in/reader034/viewer/2022042209/5eaca01565fc5b65262685b1/html5/thumbnails/34.jpg)
34
DIG
ITA
L-
Inst
itute
of I
nfor
mat
ion
and
Com
mun
icat
ion
Tec
hnol
ogie
s
JOA
NN
EU
M R
ES
EA
RC
H F
orsc
hung
sges
ells
chaf
tmbH
Ste
yrer
gass
e17
, A-8
010
Gra
z, A
US
TR
IA
E-m
ail:
hann
es.fa
ssol
d@jo
anne
um.a
tW
eb:
http
://w
ww
.joan
neum
.at/d
igita
l
Han
nes
Fas
sold
Con
tact