S a n ja y S. K a d a m
A t h e s is s u b m it t e d fo r t h e d e g r e e o f
D o c t o r o f P h ilo s o p h y
in t h e U n iv e r s it y o f L o n d o n
UCL
U n iv e r s it y C o lle g e L o n d o n
D e p a r t m e n t o f C o m p u t e r S c ie n c e
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C o m p u te r vision is a challenging ap p lica tio n for high p e rfo rm a n c e c o m p u tin g . To m eet
its c o m p u ta tio n a l d e m a n d s, a n u m b er o f SIM D an d M IM D b ased p arallel m ach in es have
been p ro p o se d an d developed. H ow ever, d ue to high co sts an d long te rm design tim e s
th e se m ach in es have n o t been w idely used. R ecently, n etw o rk b ased en v iro n m e n ts, such
as a c lu ste r of w o rk sta tio n s, have provided effective and econom ical p la tfo rm s fo r high
p e rfo rm a n c e c o m p u tin g . B u t developing parallel a p p lic a tio n s on such m achines involves
co m plex decisions a b o u t d is trib u tio n of processes over th e p ro cesso rs, sched u lin g o f p ro c es
sor tim e betw een c o m p e tin g processes, co m m u n icatio n p a tte r n s , etc. W ritin g ex p licit code
to co n tro l th e se decisions increases p ro g ram com p lex ity an d reduces p ro g ra m re liab ility
an d code re-usability.
W e p ro p o se a design m eth o d o lo g y b ased on design p a tte r n s w hich is in te n d e d to su p
p o rt p a ra lle liz a tio n of vision a p p lic a tio n s on a clu ste r o f w o rk sta tio n s. W e id en tify com m o n
a lg o rith m ic fo rm s o cc u rrin g re p e a te d ly in parallel vision alg o rith m s a n d fo rm u la te th e se as
design p a tte r n s . W e specify v arious a sp e c ts of p arallel b e h a v io u r of a design p a tte r n , such
as p rocess p lace m en t or c o m m u n icatio n p a tte r n s , in its d efinition or se p a ra te ly as issues
to be ad d ressed ex plicitly d u rin g its im p le m e n ta tio n . D esign p a tte r n s en su re p ro g ra m
re lia b ility an d code re -u sab ility since th e y c a p tu r e th e essence o f w orking d esigns in a
form t h a t m akes th e m u sable in different s itu a tio n s an d in fu tu re w ork.
T h e research w ork is co ncerned w ith p re sen tin g a c a ta lo g u e of design p a tte r n s to
im p le m e n t v arious form s of parallelism in vision a p p lic a tio n s on a c lu ste r of w o rk sta tio n s.
U sing re lev an t design p a tte r n s , we im p le m e n t re p re se n ta tiv e vision a lg o rith m s in low, in
te rm e d ia te an d high level vision ta sk s. M a jo rity o f th ese im p le m e n ta tio n s show p ro m isin g
re su lts. F or ex am p le, given a 512x512 im age, th e im age re s to ra tio n a lg o rith m b ased on
M ark o v ra n d o m field m odel can be co m p leted in less th a n 45 seconds on a n e tw o rk o f 16
w o rk sta tio n s (Sun S P A R C sta tio n 5). T h e sam e ta s k ta k e s m ore th a n 10 m in u te s on a
I th a n k m y su p e rv iso rs D r. G ra h a m R o b e rts a n d P ro f. B e rn a rd B u x to n for p roviding
in v alu ab le su g g estio n s, m o ral s u p p o rt an d cord ial a tm o sp h e re for c o n d u c tin g th is research
w ork a t D e p a rtm e n t of C o m p u te r Science, U n iv ersity C ollege L ondon.
I am obliged to th e A sso cia tio n of C o m m o n w ealth U niv ersities for fin an cin g m y re
sea rch w ork th ro u g h th e B ritish C ouncil in th e fo rm of C o m m o n w ealth S cholarsh ip . I
also th a n k D r. V ijay P. B h a tk a r, E x ec u tiv e D ire c to r, C -D A C (C e n te r for D ev elo p m e n t
of A dv an ced C o m p u tin g ), P u n e , In d ia, for ap p ro v in g th e re q u ired s tu d y leave (from my
c u r re n t em p lo y m en t) for co m p letio n of m y P h .D w ork.
I am g ra te fu l to m an y colleagues an d friends w ith w hom I h ad b o th ac ad em ic and
no n -a cad e m ic in te ra c tio n s. M y special th a n k s go to J o n a th a n P oole for in tro d u c in g m e to
th e c o n c e p ts in parallel an d d is trib u te d c o m p u tin g using U C -f-f (a c o n c u rre n t ex ten sio n
of C -f-|-). I also th a n k D r. J u lia S chnabel for pro v id in g m e w ith an a p p lic a tio n in m edical
im ag in g w hich served as an excellent ex am p le for p ara lle liz a tio n as discussed in c h a p te r 6.
I am also th a n k fu l to D r. N iladri C h a tte rje e for review ing th e ea rly d ra fts o f th is th esis and
p ro v id in g n u m ero u s su g g estio n s to w a rd s en h a n cin g th e tech n ical q u a lity of th e m a te ria l
p re se n te d . M an y th a n k s to K a m a le n d u P al, A rif Iqbal, A dil Q u re sh i, Ih san K h a n , C ia n n is
K oufakis, D r. Bill L an gdon an d o th e r researc h ers in th e d e p a rtm e n t for several discussions
on b o th tech n ical an d social a sp e c ts o f s tu d e n t life in L ondon.
I ex p ress u tm o s t g ra titu d e to m y p a re n ts an d re la tiv e s for all th e ir good w ishes an d
blessings. I am also in d e b te d to m y wife D r. P r a tim a for h er co n tin u o u s s u p p o rt,
en c o u ra g e m e n t an d concern for th e co m pletion o f th is research w ork. H er a d ju s tm e n ts
to th e seclusion an d fre q u e n t d isru p tio n s to th e fam ily life d u rin g th e course o f th is s tu d y
w ere highly ap p reciab le . A n d finally, th a n k s to o u r little sons K irtiR a j an d P ra n a v w ho
w ith th e ir ever cheerful a p p e a ra n c e an d in n o cen t b u t in v ig o ra tin g sm iles h elped m e to
1 In tro d u ctio n 16
1.1 O verview ... 16
1.2 A im s of th is R esearch W o r k ... 20
1.3 C o n trib u tio n s o f th e D i s s e r t a t i o n ...21
1.4 O rg a n iz a tio n of th e t h e s i s ... 22
2 P a rallelism in C o m p u ter V isio n 24 2.1 P ara lle l C o m p u t i n g ... 25
2.1.1 P ara lle l c o m p u tin g s y s t e m s ... 25
2.1.2 A lg o rith m ic c l a s s e s ... 27
2.1.3 P e rfo rm a n c e of parallel p r o g r a m s ... 28
2.2 A n O verview of C o m p u te r V i s i o n ... 29
2.2.1 O b je c t recognition in 2D s c e n e s ... 30
2.2.3 S e g m e n t a t i o n ...33
2.2.4 R e s e g m e n ta tio n ... 35
2.2.5 P ro p e rtie s an d R e la t io n s ... 35
2.2.6 O b je c t R e c o g n itio n ... 36
2.3 C o m p u ta tio n a l C h a ra c te ris tic s ...36
2.3.1 Low level p r o c e s s i n g ... 37
2.3.2 In te rm e d ia te level p r o c e s s i n g ...37
2.3.3 H igh level p r o c e s s in g ... 38
2.4 P ara lle l sy stem s for v i s i o n ... 39
2.4.1 M esh con n ected s y s t e m s ... 39
2.4.2 P y r a m i d s ...40
2.4.3 H y p e r c u b e s ...40
2.4.4 S h ared m em o ry m a c h i n e s ... 41
2.4.5 P ipelined S y stem s an d S ystolic a r r a y s ... 42
2.4.6 P a rtitio n a b le S y s t e m s ... 42
2.4.7 G e n eral p u rp o se p arallel s y s t e m s ...43
2.5 C o m p u tin g on w o rk sta tio n c l u s t e r s ...44
2.5.1 C lu ste r C o n f i g u r a t i o n ... 44
2.5.3 Use of c l u s t e r s ... 47
2.5.4 P ara lle l co m p u tin g using C lu ste rs ... 48
2.6 P a ra lle liz a tio n using D esign P a t t e r n s ...50
2.6.1 D esign p a t t e r n s ... 50
2.6.2 F o rm s of P arallelism in V ision ... 52
2.6.3 D esign p a tte r n s for parallel vision ... 55
2.7 R e la te d w o r k ... 56
2.8 S u m m a r y ... 58
3 D esig n p a ttern s for p arallelizing v ision ap p lication s 60 3.1 O rg a n iz a tio n of p a t t e r n s ... 61
3.2 D esc rip tio n o f design p a tte r n s - a te m p la te ... 63
3.3 F arm er-W o rk er P a tte r n ...65
3.4 M a ste r-W o rk e r P a t t e r n ... 70
3.5 C o n tro lle r-W o rk er P a tte r n . ...76
3.6 D iv id e-a n d -C o n q u er P a t t e r n ... 82
3.7 T em p o ra l M u ltip lex in g P a t t e r n ... 87
3.8 P ip elin e P a tt e r n ... 91
3.9 C o m p o site P ip elin e P a t t e r n ... 98
4 Low lev el alg o rith m s 106
4.1 P ara lle liz a tio n o f low level a lg o rith m s ... 109
4.2 P a rtitio n in g th e im age d a t a ...110
4.3 G re y scale t r a n s f o r m a tio n s ... 112
4.4 Im ag e filtering ...113
4.4.1 C o n v o l u tio n ... 114
4.4.2 R a n k f i l t e r i n g ... 119
4.4.3 S p a tia l f i l t e r s ... 120
4.5 F a s t F o u rier t r a n s f o r m s ... 122
4.6 Im ag e r e s t o r a t i o n ...124
4.6.1 M arkov ra n d o m field m odels for im age r e c o v e r y ...124
4.7 S u m m a r y ... 128
5 In te rm ed ia te lev el p ro cessin g 130 5.1 R egion b ased se g m e n ta tio n ... 132
5.2 P ara lle l R egion-based s e g m e n ta tio n ...134
5.3 S e g m e n ta tio n using P e rc e p tu a l O r g a n iz a tio n ...138
5.3.1 S eq u en tial Line g ro u p in g a l g o r i t h m ... 139
5.3.2 P ara lle l Line g ro u p in g a l g o r i t h m ...143
6 H igh lev el p ro cessin g 147
6.1 S eq u en tial g eo m etric h ash in g alg o rith m ... 148
6.1.1 P rep ro cessin g P h a s e ...149
6.1.2 R ecognition p h a s e ... 150
6.2 P a ra lle l g eo m etric hash in g a l g o r i t h m ... 152
6.3 M u lti-scale active sh a p e d esc rip tio n - an a p p l i c a t i o n ...157
6.3.1 A n overview of th e sh a p e d esc rip tio n p r o c e s s ... 158
6.4 P a ra lle liz a tio n of th e sh a p e d esc rip tio n p r o c e s s ... 161
6.4.1 P a ra lle liz a tio n using T em p o ra l M u ltip lex in g p a t t e r n ...162
6.4.2 P a ra lle liz a tio n using P ipeline p a t t e r n ...164
6.4.3 P a ra lle liz a tio n using C o m p o site P ip elin e p a t t e r n ... 166
6.5 S u m m a r y ... 168
7 C on clu sion 170 7.1 A im s an d M o t i v a t i o n ... 170
7.2 R esearch R e v i e w ... 172
7.3 C o n trib u tio n s of th e R esearch w o r k ... 175
7.4 C o m p a riso n w ith re la ted w o r k ... 176
7.5 F u tu re w o r k ...177
A .l P a t te r n D i a g r a m ... 180
A .2 O b je c t In te ra c tio n C h a r t s ... 181
2.1 A n overview o f a ty p ical vision based a p p l i c a t i o n ... 31
2.2 P ro c e ssin g levels in a ty p ical vision b ased a p p l i c a ti o n ... 37
2.3 A 4 -c o n n ected m esh, p y ra m id an d a 3-dim ensional h y p e rc u b e of processing e l e m e n t s ...40
2.4 S h a re d m em ory m achines (in terc o n n ecte d by a bus an d sw itch in g netw o rk ) a n d sy sto lic /p ip e lin e s y s t e m s ...43
2.5 C o m m o n clu ste r co n fig u ratio n s: bus, s ta r an d a r i n g ... 45
3.1 F a rm e r-W o rk e r P a tte r n ... 66
3.2 O b je c t In te ra c tio n in th e F arm er-W o rk er P a t t e r n ... 67
3.3 M a ste r-W o rk e r P a t t e r n ... 72
3.4 O b je c t In te ra c tio n in th e M aster-W o rk e r P a t t e r n ... 73
3.5 C o n tro lle r-W o rk er P a t t e r n ...77
3.6 O b je c t In te ra c tio n in th e C o n tro lle r-W o rk er P a t t e r n ... 78
3.7 C o n v o lu tio n m asks for finding a) h o rizo n ta l edges an d b) v ertical edges . . . 82
3.8 D C P a t t e r n ... 83
3.9 O b je c t In te ra c tio n in th e D C P a tte r n ... 84
3.10 T M P a t t e r n ...88
3.11 O b je c t In te ra c tio n in th e T M P a t t e r n ...89
3.12 Vehicle id en tificatio n s y s t e m ...92
3.13 P ip elin e P a t te r n ... 93
3.14 O b je c t In te ra c tio n in th e P ip elin e P a t t e r n ... 94
3.15 Vehicle id en tificatio n s y s t e m ...99
3.16 C o m p o site P ip e lin e P a t t e r n ... 100
3.17 O b je c t In te ra c tio n in th e C o m p o site P ip elin e P a tte r n ... 101
4.1 P a rtitio n in g of an im age, a) Row p a rtitio n in g b) Row p a rtitio n in g w ith d a t a t h a t is to be overlap p ed a n d /o r c o m m u n ic a te d ... I l l 4.2 P e rfo rm a n c e of h isto g ra m e q u a l i z a t i o n ...113
4.3 P e rfo rm a n c e of th e convolution o p e ra tio n using a 3x3 w i n d o w ...115
4.4 P e rfo rm a n c e of th e convolution o p e ra tio n using a 15x15 w indow ...116
4.5 P e rfo rm a n c e of th e convolution o p e ra tio n on a I K x lK i m a g e ...117
4.6 P e rfo rm a n c e of th e F arm er-W o rk er p a tte r n in co n v o lu tio n o p e ra tio n on vary in g th e pro cesso r load an d n u m b er of s u b ta s k s (w indow size 15x15) . . 118
4.7 P e rfo rm a n c e of th e sh a rp e n in g o p e ra tio n using sp a tia l filters (w indow size 1 1 x 1 1 ) ... 121
4.9 P e rfo rm a n c e of th e im age re s to ra tio n a lg o rith m using M R F m odel (w indow
size 3 x 3 ) ... 126
4.10 P e rfo rm a n c e o f th e M aster-W o rk e r p a tte r n (in im age recovery o p e ra tio n
using th e M R F m odel on a 512x512 im age) s u b je c t to th e e x te rn a l load
an d load d i s t r i b u t i o n ... 128
5.1 a) P a rtitio n e d im age b) C o rre sp o n d in g q u a d tre e ...133
5.2 a) D is trib u tio n of su b im ag es b) M erging of s u b i m a g e s ... 135
5.3 P e rfo rm a n c e of th e parallel sp lit an d m erge se g m e n ta tio n alg o rith m . . . . 136
5.4 Line G ro u p in g ...139
5.5 R e la tio n a l c o n s tra in ts in th e line g ro u p in g a lg o rith m a) p ro x im ity b)
collinear-ity an d c) c o n t i n u a t i o n ...141
5.6 In d ex in g tech n iq u e used in th e line g ro u p in g process, a) search a re a for th e
base line b) th e in d ex a r r a y ... 142
6.1 P re p ro c e ssin g p h ase in th e g eo m etric h ash in g a lg o rith m a) O rth o g o n a l co
o rd in a te sy stem defined by th e basis se t b) A d d in g (m o d e l, b a sis) p a irs in
th e h ash t a b l e ...150
6.2 R ecog n itio n p h ase in th e g eo m etric hash in g a lg o rith m a) O rth o g o n a l co o rd i
n a te sy ste m defined by th e basis set b) A ccessing a n d collecting (m o d e l, b a sis)
p a irs from th e h ash bins in h ash t a b l e ...151
6.3 H ash ta b le d a ta s tru c tu r e a) sy m m etric ind ex in g in h a sh ta b le b) h ash
en trie s in n o rm al h ash ta b le c) re d u ctio n in h a sh e n trie s using sy m m e trie s . 154
6.5 M u lti-scale sh a p e d esc rip tio n process a) p ro p a g a tio n s te p ap p lied on a se t
o f five im age slices b) m u lti-scale sh a p e sta c k of an im ag e slice c o m p u te d
in th e sh a p e focusing s te p (F ig u re (b) a d a p te d from (S chnabel, 1997)). . . . 160
6.6 S h ap e focusing p erfo rm ed a t different scales in th e im ag e scale-space of an
im age slice using activ e c o n to u r m odels: (a) cr = 8 (b) a = 4 (c) a = 2 (d)
(7 = 1. Im age (a) also c o n ta in s th e in itial c o n to u r su p e rim p o se d in black.
All im ages are ta k e n from (S chnabel, 19 9 7 )... 160
6.7 V isu aliza tio n o f th e sta c k c o n to u rs (th o se displayed in F ig u re 6.6) stack e d
using tria n g u la tio n . Im age ta k e n from (S chnabel, 1 9 9 7 )... 161
6.8 P a ra lle liz a tio n of th e sh a p e d esc rip tio n process using a P ip e lin e p a tte r n .
T h e in te g e r values d e n o te seq u e n tia l tim e (in seconds) re q u ired for e x e cu tin g
c o rresp o n d in g co m p o n en ts of th e P ipeline p a t t e r n ... 164
6.9 P a ra lle liz a tio n of th e m ulti-scale sh a p e d esc rip tio n pro cess using a C o m
4.1 E x e c u tio n tim e in (m in :se c) foT h isto g ram e q u a li z a t io n ... 113
4.2 E x ec u tio n tim e in (m in :sec) for th e convolution o p e r a t i o n ... 114
4.3 P e rfo rm a n c e of th e F arm er-W o rk er p a tte r n on v ary in g th e e x te rn a l load an d n u m b e r of su b ta sk s. T h e execution tim e (m in ise c ) d isplayed are for th e convolution o p e ra tio n (w indow size 1 5 x15)... 118
4.4 E x e c u tio n tim e in (m in ise c ) for th e ra n k filterin g o p e r a t i o n ... 120
4.5 E x ec u tio n tim e in (m in :se c) ï o t th e sh a rp e n in g o p e ra tio n ... 121
4.6 E x ec u tio n tim e in (m in :se c) fov F F T o p e r a t i o n ...123
4.7 E x ec u tio n tim e in (m in :sec) fo i im age re s to ra tio n using M R F m odel . . . . 125
4.8 P e rfo rm a n c e of th e M aster-W o rk e r p a tte r n w hen su b je c te d to th e e x te rn a l load. T h e ex e cu tio n tim es (m in :se c) displayed are for th e im age re s to ra tio n o p e ra tio n using th e M R F m odel on a 512x512 im a g e ... 127
5.1 E x ec u tio n tim e in (m in :se c) for th e p arallel sp lit an d m erge se g m e n ta tio n a l g o r i t h m ... 136
5.2 E x ec u tio n tim e in (m in isec ) for various o p e ra tio n s in th e p arallel sp lit an d m erge se g m e n ta tio n alg o rith m applied on a 5 12X 512 i m a g e ...137
5.3 E x ec u tio n tim e in (m in ise c ) for th e line g ro u p in g process ...144
6.1 E x e c u tio n tim e in (m in :se c) for th e g eo m etric h ash in g a l g o r i t h m ... 156
6.2 E x ec u tio n tim e s in (seconds) iov different im p le m e n ta tio n s an d in d iv id u a l
In tr o d u c tio n
1.1
O v e r v ie w
C o m p u te r V ision d eals w ith th e principles and tech n iq u e s to e x tr a c t an d in te rp re t useful
in fo rm a tio n in a scene by c a p tu rin g an d analyzing im ages o f t h a t scene. It h as ap p lic a tio n s
in several a re a s such as re m o te sensing, au to n o m o u s vehicle g u id an ce , in d u s tria l in sp ectio n ,
an d m edical im aging. Som e of th ese ap p lica tio n s, such as a u to n o m o u s vehicle g u id an ce ,
are real-tim e an d involve a lg o rith m s w hich m u st co m p lete th e ir c o m p u ta tio n s w ith in a
fra c tio n of a second. Som e ap p lica tio n s req u irin g h u m an in te ra c tio n are in te ra c tiv e and
m u st co m p lete w ith in few seconds or less, d ep e n d in g on th e ty p e o f in te ra c tio n re q u ired .
O th e r ap p lic a tio n s are batch ap p lica tio n s which can to le ra te m ax im u m la te n c y o f few
h o u rs or even d ay s. T h e n a tu re of th e a lg o rith m s involved in th e se ap p lic a tio n s is th u s
v aried. B u t m o st of th e se a lg o rith m s are c o m p u ta tio n a lly in ten siv e an d re q u ire en o rm o u s
c o m p u tin g pow er for th e ir p ra c tic a l im p le m e n ta tio n .
C o m p u te r vision uses a b ro a d sp e c tru m of alg o rith m s covering different a re a s such as
im ag e a n d signal processing, g ra p h th eo ry , m a th e m a tic s, a n d artific ia l intelligence. F ro m
a c o m p u ta tio n a l p ersp ectiv e, vision processing is co n veniently classified in to th re e levels:
low, in te rm e d ia te , and high. Low level processing involves pixel-based tra n s f o rm a tio n s
w here uniform c o m p u ta tio n s are applied a t each pixel or a n e ig h b o rh o o d a ro u n d each
pixel in th e im ag e d a ta . T h ese c o m p u ta tio n s are m ain ly n u m eric an d well s tr u c tu r e d .
In te rm e d ia te level p rocessing involves b o th num eric an d sym bolic c o m p u ta tio n s . It com
prises a lg o rith m s to form regions of in te re st in th e im age d a ta , such as g ro u p in g of low level
fe a tu re s (e.g. edges) in to lines, arcs, o r re c ta n g u la r b o rd e rs of an o b je c t. H igh level pro cess
ing involves sym bolic c o m p u ta tio n s w here d a t a p rovided by th e low a n d in te rm e d ia te level
a lg o rith m s is used for te s tin g an d g e n e ra tin g h y p o th eses for o b je c t reco g n itio n . A ty p ic a l
vision a p p lic a tio n com prises low, in te rm e d ia te a n d high level vision ta s k s /a lg o r ith m s an d
th u s involves b o th num eric an d sym bolic c o m p u ta tio n s. T h erefo re, a lth o u g h vision has
been identified as a grand challenge ap p lica tio n for high p e rfo rm a n c e co m p u tin g , com
p u ta tio n a l c h a ra c te ristic s o f vision ap p lica tio n s are differen t fro m th e s tru c tu r e d n u m b e r
c ru n ch in g c o m p u ta tio n s arising in m o st o th e r g ra n d challenge a p p lic a tio n s (W ang e t al.,
1996).
To m eet th e c o m p u ta tio n a l d e m a n d s of vision ta sk s, several effo rts have been d ire c te d
to w a rd s p roviding a high p erfo rm an c e c o m p u tin g s u p p o rt for th e ir p ra c tic a l im p le m e n
ta tio n . A b rief su rv ey of th e research efforts in high p e rfo rm a n c e parallel c o m p u tin g for
vision can be fo u n d in (W ebb, 1994). T h ese efforts can b ro a d ly be g ro u p e d in to follow
ing ca te g o ries, based on th e ty p e of co m p u tin g p la tfo rm s th e y utilize: sp ec ia l-p u rp o se
h a rd w a re chips, SIM D b ased m achines, specialized vision sy ste m s an d g en e ral p u rp o se
p arallel m achines. S p ecial-p u rp o se h a rd w a re chips serve as a c c e le ra to rs to specific vision
alg o rith m s since th e y im p le m e n t th e c o m p u ta tio n s in h a rd w a re . H ow ever, th e y are s u ita b le
only for specific w e ll-stru c tu re d low level vision a lg o rith m s, such as im age co n v o lu tio n .
S IM D based m u ltip ro c esso r m achines such as m eshes, a rra y p ro cesso rs, h y p e rc u b e s,
an d p y ra m id s, co n sist of sim ple processing elem en ts co n n e cted by a c o m m u n ic a tio n n e t
w ork. T h e se m achines p erfo rm well for im p le m e n tin g m o st of th e low level vision algo
rith m s . B u t th e y a re n o t well su ited for high level vision a lg o rith m s since th e se a lg o rith m s
involve n o n u n ifo rm processing an d com plex d a t a s tru c tu re s . S pecialized vision sy ste m s are
special p u rp o se p arallel m achines designed to su it th e re q u ire m e n ts o f vision ta sk s. T h e y
are c a p a b le of bein g p a rtitio n e d in to one or m ore in d e p e n d e n t S IM D an d M IM D s u b
sy ste m s to m a tc h th e c o m p u ta tio n a l c h a ra c te ristic s o f vision a lg o rith m s a t v ario u s levels.
F o r ex am p le, th e im age u n d e rsta n d in g a rc h ite c tu re (lU A ) (W eem s e t al., 1989) h as th re e
high level vision ta sk s. Specialized vision sy stem s, how ever, have co m plex a rc h ite c tu re s
w hich involve significant design an d d ev elo p m en t effort. T h e need to develop new sy stem
s o ftw a re fo r such m achines re su lts in huge sy stem d ev e lo p m e n t costs.
G e n e ra l p u rp o se parallel m achines such as IB M S P -2, M eiko C S-2, In tel P a ra g o n ,
C ra y T 3 D , an d S G I P ow er C hallenge, have been used successfully for a v arie ty o f high
p e rfo rm a n c e c o m p u tin g ap p lica tio n s. T h ese com m ercial m achines have n o t been developed
fo r a n y specific ap p lica tio n s, b u t are m e a n t to be gen eral p u rp o se sy stem s. M o st of th e m
h av e a sim ila r a rc h ite c tu re co n sistin g of p rocessors in te rc o n n e c te d by a high speed n etw o rk .
T h e se p ro c esso rs are th o se t h a t are used in larg e u n ip ro ce sso r w o rk sta tio n s. T h ese m a
ch in es a re ty p ically org an ized as a single box t h a t c o n ta in s all th e p ro cesso r an d m em o ry
m o d u le s in te rc o n n e c te d by a special p u rp o se in te rc o n n e c tio n n etw o rk . A lth o u g h th e re
h av e b een som e a tte m p ts to use th e se m achines for p arallel vision a p p lic a tio n s (W ang,
1995), th e y are still n o t very p o p u la r w ith m an y o rg a n iz a tio n a l se tu p s.
R ecen tly , n etw o rk -b ased c o m p u tin g en v iro n m e n ts, such as a c lu ste r of w o rk sta tio n s,
hav e p ro v id ed effective an d econom ical p la tfo rm s for high p e rfo rm a n c e c o m p u tin g . A
c lu s te r o f w o rk sta tio n s offers several ad v a n ta g e s for parallelizin g an d e x e cu tin g larg e a p
p lic a tio n s on a relativ ely low -priced and readily available pool of m achines. It p rovides
m u ltip le C P U s for parallel c o m p u tin g and d ra m a tic a lly im p ro v es v irtu a l m em o ry a n d file
s y ste m p erfo rm an c e. It can ap p ro ach or exceed s u p e rc o m p u te r p e rfo rm a n c e for som e
a p p lic a tio n s and can easily be tu n e d to advances in p ro c esso r an d n etw o rk tech n o lo g y
(A n d e rso n e t al., 1995), (T u rc o tte , 1996). A c lu ste r o f w o rk s ta tio n s can in c o rp o ra te h e t
ero g e n e o u s a rc h ite c tu re s, so ap p lica tio n s can select th e m o st su ita b le c o m p u tin g reso u rces
fo r each c o m p u ta tio n .
B u t developing parallel ap p lica tio n s on such m achines involves com plex decisions a b o u t
d is trib u tio n o f processes over th e processors, process sy n c h ro n iz a tio n , scheduling of p ro c es
so r tim e b etw een c o m p e tin g processes, co m m u n icatio n p a tte r n s , e tc . W ritin g ex p licit code
to c o n tro l th ese decisions increases p ro g ram co m p lex ity a n d , red u ces p ro g ra m reliab ility
a n d co d e reusability. Also, th e available m achines a n d th e ir ca p ab ilities can vary from
o ne ex e c u tio n to a n o th e r, an d high co m m u n icatio n co sts can d e g ra d e th e p e rfo rm a n c e in
p ro g ra m m in g in o rd e r to gain th e ad v a n ta g e s of p o te n tia l p arallelism in an ap p lic a tio n .
M o st of th e m use or m odify existin g parallel code to im p le m e n t p arallelism fo r th e ir
ap p lic a tio n s. In fa c t, som e recen t surveys of experienced p arallel p ro g ra m m e rs have show n
t h a t a b o u t 69% m odify ex istin g p ro g ra m s or com pose p ro g ra m s fro m ex istin g blocks of
code. T h e re m a in in g 31% w ho s t a r t from sc ra tc h are ty p ically c o m p u te r sc ie n tists an d
ap p lied m a th e m a tic ia n s (P an c ak e, 1996),
T h e m ain goal o f th is th esis is to p re sen t a design m eth o d o lo g y b ase d on design p a tte rn s
in te n d e d to s u p p o rt p aralleliz atio n of vision ap p lica tio n s on a c lu ste r of w o rk s ta tio n s . M o st
of th e p arallel alg o rith m s used in im p lem en tin g vision ta s k s re p e a te d ly use only a U nite se t
of a lg o rith m ic form s. W e identify th e se com m on alg o rith m ic fo rm s a n d fo rm u la te th e s e as
design p a tte r n s . W e specify various a sp e c ts of parallel b eh a v io r o f a d esign p a tte r n , such
as process p la c e m e n t an d co m m u n icatio n p a tte r n s , in its defin itio n or s e p a ra te ly as issues
to be ad d re sse d ex plicitly d u rin g its im p le m e n ta tio n . D esign p a tte r n s en su re p ro g ra m
re liab ility an d code re u sab ility since th e y c a p tu re th e essence of w o rk in g d esigns in a form
t h a t m akes th e m usab le in different situ a tio n s an d in fu tu re w ork (C oplien & S c h m id t,
1995), T h e use of th e design p a tte r n s w ould enable d ev e lo p m e n t o f d is trib u te d so ftw a re
quickly, econom ically an d reliably. Using a clu ste r of w o rk sta tio n s, re se a rc h e rs can use th e
design p a tte r n s to im p le m e n t m an y in te ra c tiv e an d b a tc h a p p lic a tio n s in c o m p u te r vision,
A c lu s te r of w o rk sta tio n s is c h a rac te rized by high c o m m u n ic a tio n c o sts an d a v a ria
tio n in speed fa c to rs of individual m achines in th e n etw o rk . W e need to ad d re ss th e se
issues w hile fo rm u la tin g th e design p a tte r n s . O ne fa c to r t h a t m inim izes th e effect of high
co m m u n ic a tio n co sts on p erfo rm an c e is granularity. G ra n u la rity o f an a lg o rith m d esc rib es
th e a m o u n t o f w ork asso c ia te d w ith each ta s k re la tiv e to c o m m u n ic a tio n . A n a lg o rith m
t h a t ex c h an g es d a t a betw een its processes a fte r a sm all n u m b e r o f c o m p u ta tio n s is called
fin e -g ra in e d w hile an alg o rith m w here th e c o m p u ta tio n s c o n tin u e for a long tim e befo re
th e c o m m u n ic a tio n is req u ired is te rm e d as coarse-grained. Since a c lu s te r of w o rk s ta tio n s
is in h e re n tly co a rse-g rain ed , we need to fo rm u la te design p a tte r n s so t h a t th e y im p le m e n t
co a rse g ra in e d parallelism . Also, th e design p a tte r n s sho u ld d is tr ib u te th e w ork lo ad
ac co rd in g to th e speed fa c to rs of in d iv id u al m achines in th e n etw o rk .
of vision a lg o rith m s. We id en tify v arious form s of parallelism in vision a lg o rith m s an d
fo rm u la te design p a tte r n s to im p le m e n t th e m . E ach design p a tte r n c a p tu re s co m m o n
designs used by d ev elopers to parallelize th e ir a p p lic a tio n s. W e p re se n t a c a ta lo g u e of
design p a tte r n s to im p le m e n t various form s of parallelism in vision a p p lic a tio n s on a
c lu ste r o f w o rk sta tio n s. U sing relev an t design p a tte r n s , we im p le m e n t re p re s e n ta tiv e vision
a lg o rith m s in low, in te rm e d ia te an d high level vision ta sk s, an d p re se n t th e e x p e rim e n ta l
re su lts o f th e c o rresp o n d in g parallel im p le m e n ta tio n s.
In low level, we im p le m e n t a lg o rith m s such as h isto g ra m eq u a liz a tio n , co n v o lu tio n ,
im age filterin g using s p a tia l filters, an d im age re s to ra tio n using M ark o v ra n d o m field
m odels. In in te rm e d ia te level, we im p le m e n t reg io n -b ased sp lit a n d m erge se g m e n ta tio n
a lg o rith m an d line g ro u p in g alg o rith m based on p rinciples of p e rc e p tu a l g ro u p in g . In
high level, we im p le m e n t g eo m etric hash in g a lg o rith m for o b je c t reco g n itio n . W e also
discuss p aralleliz atio n of an ap p lica tio n in m edical im aging, nam ely, m u lti-scale ac tiv e
s h a p e d e sc rip tio n o f M R (m ag n e tic resonance) b ra in im ag es using ac tiv e c o n to u r m odels.
1.2
A im s o f th is R e se a r c h W ork
T h e focus of th e w ork in th is th esis is to develop m eth o d o lo g ies to s u p p o rt p ara lle liz a tio n
of vision ap p lic a tio n s on a clu ste r of w o rk sta tio n s. T h e m ain goals of th is th esis w ork are:
• To an aly ze c o m p u ta tio n a l ch a ra c te ristic s o f vision ta s k s a n d identify com m o n algo
rith m ic s tru c tu re s in th e ir parallel im p le m e n ta tio n s.
• To c a p tu r e an d a rtic u la te th ese alg o rith m ic s tru c tu re s as design p a tte r n s in a form
t h a t m akes th e m usable in different situ a tio n s an d in fu tu re w ork.
• To use th e se design p a tte r n s for im p lem en tin g som e re p re s e n ta tiv e vision a lg o rith m s
in low, in te rm e d ia te an d high level vision processing.
• To ev a lu a te th e v iab ility o f using a clu ste r of w o rk s ta tio n s to parallelize vision
1 .3
C o n tr ib u tio n s o f th e D is s e r ta tio n
T h e c o n trib u tio n s o f th is d isse rta tio n are three fold. F irstly , we p ro p o se a design m e th o d
ology based on design p a tte r n s in ten d ed to s u p p o rt p aralleliz atio n of vision a p p lic a tio n s
on a c lu ste r of w o rk sta tio n s. Secondly, we p re sen t co arse-g rain ed p arallel a lg o rith m s for
so m e re p re se n ta tiv e vision a lg o rith m s in low, in te rm e d ia te a n d high level vision pro cess
ing. T h ird ly , we use re le v an t design p a tte r n s to im p le m e n t th e se p arallel a lg o rith m s on
w o rk s ta tio n c lu ste rs. T h ese c o n trib u tio n s are su m m arize d as follows:
• D esign p a tte r n s : W e identify com m on a lg o rith m ic s tru c tu re s o cc u rrin g re p e a te d ly
in p arallel vision ta s k s / ap p lica tio n s an d fo rm u la te th e se as design p a tte r n s . We
d esc rib e each design p a tte r n using a tem p la te w hich o u tlin e s in te n t, m o tiv a tio n ,
s tru c tu re , in te ra c tio n a m o n g st th e c o m p o n e n ts an d ap p lic a b ility of th e design p a t
te rn . T h is d esc rip tio n en ables selection an d use of a design p a tte r n in different
s itu a tio n s a n d in fu tu re work.
• C o arse -g rain e d parallel a lg o rith m s: We p re se n t co a rse-g rain ed parallel a lg o rith m s
an d im p le m e n ta tio n s for several vision ta s k s such as co n v o lu tio n , im ag e filterin g ,
im age re s to ra tio n , region-based se g m e n ta tio n , line g ro u p in g , a n d g eo m etric h ash in g
a lg o rith m for o b je c t recognition. W e also p re se n t different p arallel im p le m e n ta tio n s
of th e m ulti-scale activ e sh a p e d esc rip tio n process (an a p p lic a tio n in m edical im ag
ing) using different design p a tte rn s .
• Im p le m e n ta tio n on a c lu ste r of w o rk sta tio n s: U sing re le v an t design p a tte r n s , we
p erfo rm parallel im p le m e n ta tio n s of th e selected re p re se n ta tiv e vision ta s k s s ta te d
above. T h e re su lts of th e se im p le m e n ta tio n s en ab le critica l asse ssm e n t o f th e design
p a tte r n s for achieving im p ro v em en ts in a p p lic a tio n p erfo rm an c e. It also en ab les
e v a lu a tin g th e v iab ility of using w o rk sta tio n clu ste rs for im p le m e n tin g p arallel vision
1 .4
O r g a n iz a tio n o f th e th e s is
T h e re m a in d e r of th e th esis is org an ized as follows:
• C h a p te r 2 review s co n c ep ts an d m e th o d s in several d ifferen t a re a s re la te d to th e
p arallel vision sy stem s. W e begin w ith a brief in tro d u c tio n to p arallel c o m p u tin g
sy ste m s a n d parallel a lg o rith m s. W e th e n describe g en e ral principles an d m e th o d s
used in th e field of c o m p u te r vision, giving specific e m p h a sis on a p p lic a tio n s in
volving an a ly sis of 2D scenes. W e also describ e th e c o m p u ta tio n a l c h a ra c te ris tic s
o f vision a lg o rith m s an d o u tlin e SIM D an d M IM D b ased p arallel m ach in es used for
p arallelizin g th e se a lg o rith m s. W e th e n describe parallel c o m p u tin g on w o rk sta tio n
c lu ste rs an d discuss th e ir ad v a n ta g e s over th e co n v e n tio n a l parallel m ach in es. We
p re se n t vario u s form s of p arallelism in vision a lg o rith m s a n d in tro d u c e th e co n c e p t of
design p a tte r n s in te n d e d to s u p p o rt p aralleliz atio n of vision a p p lic a tio n s on a c lu ste r
o f w o rk sta tio n s. F inally, we o u tlin e som e of th e leading research efforts re la te d to
th e w ork p re sen ted in th is thesis.
• C h a p te r 3 p re se n ts a d etailed d esc rip tio n of each design p a tte r n . W e use a te m
p la te to specify v arious a sp e cts o f parallel beh av io r (such as process p lace m en t
an d c o m m u n icatio n p a tte r n s ) of each design p a tte r n . T h e te m p la te s o u tlin e in te n t,
m o tiv a tio n , s tru c tu re , in te ra c tio n a m o n g st th e c o m p o n e n ts a n d a p p lic a b ility of th e
design p a tte r n s .
• C h a p te r 4 discusses p aralleliz atio n of som e low level vision a lg o rith m s such as his
to g ra m eq u a liz atio n , co nvolution, im age sh a rp e n in g using s p a tia l filters, fa s t fo u rie r
tra n sfo rm s, an d im age re sto ra tio n using M arkov ra n d o m field m odels. E ach algo
rith m is p arallelized by using e ith e r F arm er-W o rk er, M aste r-W o rk e r o r C o n tro lle r-
W o rk er p a tte r n .
• C h a p te r 5 p re se n ts re su lts of p aralleliz atio n of som e in te rm e d ia te level a lg o rith m s
such as regio n -b ased se g m e n ta tio n , and line g ro u p in g a lg o rith m based on th e p rin ci
ples of p e rc e p tu a l o rg a n iz a tio n . W e use D iv id e -a n d -C o n q u e r p a tte r n for im p le m e n t
ing th e parallel region-based se g m e n ta tio n a lg o rith m . T h e line g ro u p in g a lg o rith m
• C h a p te r 6 p re se n ts re su lts o f p a ralleliz atio n of a high level vision a lg o rith m , nam ely,
g eo m etric h ash in g for o b je c t recognition. W e use a F a rm e r-W o rk e r p a tte r n to p e r
form m u ltip le m a tc h in g o p e ra tio n s (probes) for id en tify in g each o b je c t in an im age.
In th e la s t section o f th is c h a p te r, we discuss p a ra lle liz a tio n o f an a p p lic a tio n in
m edical im aging, nam ely, m ulti-scale ac tiv e sh a p e d e sc rip tio n of M R (m a g n e tic
resonance) b ra in im ages using activ e c o n to u r m odels. W e d iscuss th re e differen t
ap p ro a c h e s of p arallelizing th e sh a p e d esc rip tio n process. E ach a p p ro a c h uses a
different design p a tte r n , nam ely. T em p o ra l M u ltip lex in g , P ip elin e or C o m p o site
P ipeline.
P a ra llelism in C o m p u ter V isio n
C o m p u te r vision is a challenging a p p lica tio n for high p e rfo rm a n c e co m p u tin g . M an y
vision a p p lic a tio n s are c o m p u ta tio n a lly intensive an d involve co m p lex processing. F o r a
p ra c tic a l an d re a l-tim e im p le m e n ta tio n of vision a p p lica tio n s, h ig h -p e rfo rm a n c e c o m p u tin g
s u p p o rt is essen tial. O ver th e p a s t several years, parallel p rocessing h as been perceived to
be an a ttr a c tiv e an d econom ical way to achieve th e required level of p e rfo rm a n c e in vision
ap p lic a tio n s. C o m p u ta tio n a l d e m a n d s an d re al-tim e c o n s tra in ts asso c ia te d w ith th e vision
ap p lic a tio n s have induced several research efforts to explore th e use of parallel c o m p u tin g
reso u rces for p arallelizing vision a p p lica tio n s (W ebb, 1994). M o st vision a p p lic a tio n s
co n sist of im age p re p ro ce ssin g followed by o b je c t id en tificatio n . A lth o u g h b o th th e se ta s k s
involve larg e n u m b e r of c o m p u ta tio n s, th e y em b o d y d ifferent c o m p u ta tio n a l p a ra d ig m s.
A s a re su lt, sev eral special an d gen eral p u rp o se parallel m ach in e s have been p ro p o se d ,
developed an d used in im p le m e n tin g parallel so lu tio n s to m an y vision a lg o rith m s.
T h is c h a p te r gives an overview of th e a lg o rith m s in c o m p u te r vision a n d p re se n ts
parallel sy stem s an d m ethodologies used in parallelizing vision a p p lic a tio n s. T h e c h a p te r
is o rg an ized as follows. Section 2.1 in tro d u c e s som e co n c ep ts in p arallel co m p u tin g . Sec
tio n 2.2 gives an overview of th e principles and m e th o d s involved in th e field o f c o m p u te r
vision. Section 2.3 discusses c o m p u ta tio n a l c h a ra c te ristic s of vision a p p lic a tio n s and th e ir
classification in to th re e levels, low, in te rm e d ia te an d high. S ection 2.4 o u tlin e s different
p arallel sy ste m s used for parallelizing vision a p p lica tio n s. S ectio n 2.5 d escrib es p arallel
co m p u tin g on a c lu ste r of w o rk sta tio n s. Section 2.6 p ro p o se s a m eth o d o lo g y , b ased on
design p a tte r n s , w hich can be used to parallelize a m a jo rity o f th e vision a p p lic a tio n s on
n e tw o rk -b ased m achines, such as a clu ste r of w o rk sta tio n s. W e also d esc rib e v ario u s form s
of p arallelism t h a t can be applied to parallelize vision a p p lic a tio n s. F inally, sec tio n 2.7
o u tlin e s som e o f th e lead in g research efforts w hich hav e been in sp ira tio n a l to th e w ork
p re se n te d in th is thesis.
2.1
P a r a lle l C o m p u tin g
P a ra lle l c o m p u tin g is concerned w ith apply in g m u ltip le p ro c esso rs to solve a single com
p u ta tio n a l p roblem for achieving b e tte r p erfo rm an ce. T h is sectio n begins w ith an in
tro d u c tio n to p arallel co m p u tin g sy stem s. It is followed by a d esc rip tio n o f a b s tr a c t
a lg o rith m ic classes c h a ra c te riz in g d ifferent parallel alg o rith m s. T h ese classes are useful
w hen d iscussing alg o rith m s a t a higher level.
2 .1 .1 P a r a lle l c o m p u t in g s y s t e m s
A parallel c o m p u te r is a collection of processors an d m em o ry c o n n e cted by som e ty p e of
co m m u n ic a tio n n etw o rk . P ara lle l co m p u tin g sy ste m s in clu d e a full s p e c tru m o f sizes an d
prices, from a collection of w o rk sta tio n s a tta c h e d to a lo c a l-a re a n etw o rk , to an expensive
h ig h -p erfo rm an c e m achine w ith th o u sa n d s of p ro cessors c o n n e cted by h igh-speed sw itches
(D u n c a n , 1992).
T h e a rc h ite c tu re s of th e co m p u tin g sy stem s are co m m o n ly o rg a n iz ed in te rm s of in
s tru c tio n s tre a m s an d d a t a s tre a m s (F lynn, 1972). T h e th re e cases t h a t hav e becom e
fa m iliar te rm s to th e parallel p ro g ra m m e r are SISD (single in s tru c tio n , single d a ta ) , S IM D
(single in s tru c tio n , m u ltip le d a ta ) an d M IM D (m u ltip le in s tru c tio n , m u ltip le d a ta ) . SISD
c o m p u te rs are th e tra d itio n a l von N eum ann c o m p u te rs t h a t have a single in s tru c tio n
stre a m a n d a single d a ta s tre a m . All o p e ra tio n s on th ese c o m p u te rs are logically seq u e n
tial. In a SIM D parallel c o m p u te r a single in stru c tio n s tre a m is ap p lied to m u ltip le d a t a
c o n n e cted by an in te rc o n n e c tio n netw o rk . T h e M IM D d a t a m odel is th e m o st g en e ral
m odel of a p arallel c o m p u te r. A M IM D c o m p u te r h as m u ltip le pro cessin g elem e n ts each
of w hich is a co m p lete c o m p u te r in its own rig h t.
A lth o u g h SIM D sy ste m s are easy to p ro g ram , o p tim izin g S IM D p ro g ra m s to yield
a c c e p ta b le p erfo rm an c e is very difficult. As a re su lt, SIM D c o m p u te rs have n o t been very
p o p u la r for scientific c o m p u tin g . T h is m akes M IM D sy stem s th e overw helm ing m a jo rity
o f p arallel sy stem s, especially w hen a clu ste r o f w o rk sta tio n s is view ed as a single M IM D
c o m p u te r. A M IM D c o m p u te r co n sists o f p ro cessors an d m em ory. T h e m em o ry can be
e ith e r sh a re d or d is trib u te d am ong th e processors. W e can th e re fo re con sid er tw o d is tin c t
p ro g ra m m in g m odels: sh a re d m em ory M IM D and d is trib u te d m em o ry M IM D . H ow ever,
since th e sam e issues of d a t a locality an d co n c u rre n cy arise in b o th th e cases, we can
view M IM D c o m p u te r in te rm s of a com m on p ro g ra m m in g m odel. O ne such m odel is
th e coordination m odel (M a ttso n , 1996), (F o ster, 1995), w here a p arallel c o m p u ta tio n is
viewed as a collection of d is tin c t processes w hich in te ra c t a t d isc re te p o in ts th ro u g h a
c o o rd in a tio n o p e ra tio n . T h e te rm coordination refers to th e b asic o p e ra tio n s to c o n tro l a
parallel c o m p u te r. It includes c o o rd in a tio n o p e ra tio n s for in fo rm a tio n exch an g e, process
sy n c h ro n iz a tio n an d process m a n a g e m e n t. T h ese c o o rd in a tio n o p e ra tio n s m ay v ary in
speed a n d s tru c tu re , how ever, th e overall m odel is essen tially th e sam e.
B u t describ in g parallel an d d is trib u te d c o m p u te rs in te rm s o f a c o o rd in a tio n m odel
is n o t u n iversally accep ted like th e von N e u m an n m odel (M a tts o n , 1996). H ow ever, such
a m odel can be s ta te d and used for p ro g ram m in g parallel c o m p u te rs w ith in a un iv ersal
p ro g ra m m in g m odel. A lth o u g h th e c o m p u te r sy stem s differ, th e difference is g ra n u la rity
(ra tio o f c o m p u ta tio n to co m m u n icatio n ), and n o t th e fu n d a m e n ta l p ro g ra m m in g m odel
(M a tts o n , 1996).
T h e p ro g ra m m in g m odel, in o rd e r to be useful, m u st be im p le m e n te d as a p ro g ra m m in g
en v iro n m e n t. T h e re are several p ro g ram m in g e n v iro n m e n ts s u p p o rtin g v ario u s in c a rn a
tio n s of th e c o o rd in a tio n m odel which ru n well on parallel c o m p u te rs as well as a c lu ste r of
w o rk sta tio n s (T u rc o tte , 1993), (C heng, 1993). O ne can develop a p arallel code using som e
high level lan g u ag e designed specifically to s u p p o rt parallel an d d is trib u te d c o m p u tin g .
(often called as m essage-passing lib ra ry ), such as P V M (S u n d eram , 1990).
P ro g ra m s w ritte n for parallel M IM D sy stem s fall in to tw o ca te g o ries: S P M D (single
p ro g ra m m u ltip le d a ta ) an d M P M D (m ultiple p ro g ra m m u ltip le d a ta ) . F o r S P M D p ro
g ra m s, each p ro cesso r ex ecu tes th e sam e o b je c t code. S P M D sty le o f p ro g ra m m in g is easy
to code since th e p ro g ra m m e r needs to m a in ta in a single so u rce code. In c o n tra s t, M P M D
p ro g ra m s allow each pro cesso r to have a d istin c t ex e cu tab le code. A p ro g ra m m e r can sp lit
th e p ro g ra m in to different m odules w hich can be developed an d deb u g g ed in d e p e n d e n tly
or reu sed as c o m p o n e n ts o f o th e r p ro g ram s. A M P M D p ro g ra m re q u ires less m em o ry
co m p a re d to its equ iv alen t S P M D version (M a ttso n , 1996).
2 .1 .2 A lg o r it h m ic c la ss e s
M o st of th e parallel a lg o rith m s can be classified in te rm s of th e re g u la rity of th e u n d e r
lying d a t a s tru c tu re s (space) an d th e sy n ch ro n iz atio n req u ired as th e se d a t a elem e n ts are
u p d a te d (tim e) (A ngus e t ah , 1989), (M a ttso n , 1996). B ased on th is classification schem e
th e re are fo u r g en e ral classes of parallel alg o rith m s:
1. S y n ch ro n o u s
S y n ch ro n o u s alg o rith m s are th o se in w hich re g u la r d a t a elem e n ts a re u p d a te d a t
re g u la r in terv a ls of tim e . T h ey are re g u la r in sp ace an d re g u la r in tim e . T h e y involve
tig h tly coupled m an ip u la tio n of identical d a t a elem en ts. S y n ch ro n o u s a lg o rith m s
can b e expressed in te rm s of a single in stru c tio n s tre a m , an d are th e re fo re easily m a p p e d o n to SIM D c o m p u te rs. T h e p arallelism is u su ally ex p ressed in te rm s of th e
dec o m p o sitio n of th e d a ta . In fa c t, th e d a t a drives th e p arallelism , hence th e n a m e
data parallelism . H ow ever, d a t a parallelism is m ore g en e ral th a n SIM D p arallelism ,
since d a t a parallelism does n o t in sist on a single in s tru c tio n s tre a m .
2. Loosely sy n ch ro n o u s
A loosely sy n ch ro n o u s alg o rith m sy n ch ro n o u sly u p d a te s d a t a elem e n ts w hich differ
from one p ro cesso r to a n o th e r. Loosely sy n ch ro n o u s a lg o rith m s are re g u la r in tim e
ch ro n o u s case. H ow ever, d u e to v a ria tio n in th e d a t a elem e n ts across th e processo rs,
th e w ork loads can v ary from pro cesso r to processor. H ence, loosely sy n ch ro n o u s
alg o rith m s need som e m echanism to b alan ce th e c o m p u ta tio n a l load a m o n g th e
pro cesso rs of th e parallel c o m p u te r.
3. A sy n ch ro n o u s
A sy n ch ro n o u s a lg o rith m s do n o t have re g u la r d a t a u p d a te s , so th e sy ste m p ro ceed s
w ith no n u n ifo rm an d so m etim e s ra n d o m sy n c h ro n iz a tio n . T h ese a lg o rith m s are
irre g u la r in tim e an d usually irre g u la r in space w ith u n p re d ic ta b le or n o n e x iste n t
coupling b etw een th e ta sk s. T h is class o f problem s, o th e r th a n th e e m b a rra ssin g ly
p arallel s u b se t described n ex t, is m o st ra re. T h is is b ecau se p ro g ra m s for im p le
m e n tin g a sy n c h ro n o u s a lg o rith m s are difficult to c o n s tru c t. W hile sy n ch ro n o u s
a n d loosely sy n ch ro n o u s alg o rith m s are usually p arallelized by focusing on d a t a
dec o m p o sitio n , asy n c h ro n o u s a lg o rith m s are usu ally parallelized by d e c o m p o sitio n
of th e co n tro l, w hich is referred to as fu n c tio n a l or control parallelism .
4. E m b a rra ssin g ly parallel
E m b a rra ssin g ly parallel a lg o rith m s are th o se asy n c h ro n o u s a lg o rith m s for w hich th e
ta s k s are co m p letely in d e p e n d e n t and uncoupled. T h e parallelism in th is case is
triv ia l an d th e p ro g ra m s are am o n g th e sim p le st p arallel p ro g ra m s to c o n s tru c t.
P ro b le m s in th is class are very com m on in p arallel c o m p u tin g since th e ir c o m p u
ta tio n s easily m a p in to th is m odel. In fa c t, any p ro g ra m co n sistin g of a loop w ith
co m p u te -in te n siv e a n d in d e p e n d e n t ite ra tio n s can be p arallelized using th is m odel.
E m b a rra ssin g ly parallel p ro g ra m s usually utilize an S P M D sty le o f p ro g ra m m in g
com bined w ith som e m echanism for load b alan cin g . L oad b alan c in g schem es can
e ith e r be s ta tic o r d y n am ic.
2 .1 .3 P e r fo r m a n c e o f p a r a lle l p r o g r a m s
T h e m ain goal of parallelism is to reduce th e ex ecu tio n tim e of th e w hole p ro g ra m in o rd e r
to p ro d u c e th e re su lts fa ste r. T h e p erfo rm an c e e s tim a te s o f a p arallel p ro g ra m are b ased
on th e tim in g s of its co m p lete seq u e n tia l code. T h e se q u e n tia l p ro g ra m ty p ically co m p rises
T h e p arallel c o n te n t p o f th e p ro g ram is defined as th e ra tio of th e tim e ta k e n to e x e c u te th e
p o te n tia lly parallel code u p o n th e tim e ta k e n to ex ecu te th e w hole code. T h e m ax im u m
th e o re tic a l sp ee d u p t h a t can be achieved for a given p ro g ra m is a fu n c tio n of th e parallel
c o n te n t p an d th e n u m b er of processo rs t h a t will be used {N). It is given by A m d a h l’s law
(A m d a h l, 1988) w hich is s ta te d as follows
Theoretical speedup = —— . ^ (2.1)
T h e th e o re tic a l sp ee d u p is lower th a n th e ideal speedup^ w hich reflects th e ideal t h a t
ap p ly in g N p ro cesso rs to a p ro g ra m should cause it to co m p lete N tim e s fa s te r. T h e size
of th e g a p b etw een th e ideal an d th e o re tic a l sp ee d u p is a fu n c tio n of th e serial c o n te n t of
th e p ro g ra m . T h is su g g ests t h a t th e a m o u n t of sp ee d u p t h a t can be achieved for every
p ro g ra m is lim ited beyond a c e rta in n u m b e r of processors. T h e g a p b etw een th e th e o re tic a l
an d ideal sp e e d u p m ay ch ange d ue to th e increase in problem size (e.g. w hen n u m b e r of
ite ra tio n s are in cre ased in a sim u la tio n ). T h e g ap n arro w s dow n w hen th e p arallel c o n te n t
o f th e p ro g ra m increases d u e to increase in problem size, while th e g a p m ay a c tu a lly w iden
if th e le n g th of th e serial b o ttle n eck s also increase upo n increase in p roblem size. H ow ever,
th e th e o re tic a l sp ee d u p is ra rely achievable by a parallel a p p lic a tio n . T h e re will a c tu a lly
be an observed speedup w hich is m uch lower th a n th e th e o re tic a l sp ee d u p , reflecting th e
effect o f e x te rn a l o verhead on th e to ta l execu tio n . T h is o v erh ead com es from tw o so u rces
a) th e a d d itio n a l processor cycles exp en d ed in sim ply m an ag in g th e parallelism b) w a ste d
tim e s p e n t w a itin g for I /O , co m m u n icatio n am o n g processors, a n d , c o m p e titio n from th e
o p e ra tin g sy ste m an d o th e r users (P an c ak e, 1996). T h e o re tic a l sp e e d u p d o es n o t ta k e
th e se fa c to rs in to ac co u n t.
2 .2
A n O v e r v ie w o f C o m p u te r V isio n
T h e basic in p u t in c o m p u te r vision is a se t of one or m ore im ag e(s) o f som e scene, w hile
th e o u tp u t is a d esc rip tio n o f th e o b je c ts in t h a t scene. A n im ag e, c a p tu re d by a sensor,
is an a rra y of n u m b e rs called pixels t h a t re p re se n t averag e b rig h tn e ss (gray level) or color
in te g e r h av in g 256 d is tin c t values, w hile each color value is re p re se n te d by a n-valued tu p le
m easu rin g b rig h tn e ss in a se t of n -sp e c tra l b an d s (e.g., red, blue an d g reen ).
W e can view im age processing as a prelude to c o m p u te r vision. Im ag e p ro cessing
a lg o rith m s o p e ra te on im ages to e x tra c t an d re p re se n t scene in fo rm a tio n . H igher level
vision a lg o rith m s use scene in fo rm a tio n for o b je c t reco g n itio n an d scene in te rp re ta tio n .
C o m p u te r vision th e re fo re encom passes processing from sensing to scene in te rp re ta tio n .
T h e m ain a re a s of im age processing include im age e n h a n c e m e n t a n d resto ra tio n (to im
prove a p p e a ra n c e of an im age or to u n d o effects of im age d e g ra d a tio n s such as b lu rrin g
o r noise), im age com pressio n (to reduce an im age to sm aller se ts o f d a t a w hich can
be used for re c o n stru c tio n of an ac c e p ta b le a p p ro x im a tio n to th e original im ag e), an d
im age rec o n stru c tio n fr o m p ro jectio n s (to c o n s tru c t im ages of cross-section of an o b je c t
by an a ly z in g a se t of p ro je c tio n s ta k e n from d ifferent d ire c tio n s, as in to m o g ra p h y ).
Since m a jo rity of th e ap p lica tio n s in c o m p u te r vision involve tw o d im en sio n al (2D)
scenes a n d th e gen eral goal is to recognize o b je c ts o f in te re st in th e im ages of th e se
scenes, we will re s tric t o u r discussion to analysis of 2D scenes. T h e follow ing su b se c tio n s
o u tlin e g en eral tech n iq u e s involved in 2D o b je c t re co g n itio n . A d e ta ile d discussion dealin g
p rim a rily w ith 2D vision can be found in (B allard & B ro w n , 1982), (R osenfeld & K ak,
1982), (S onka e t al., 1993), w hile an o u tlin e of b o th 2D a n d 3D vision is given in (R osenfeld,
1988).
2 .2 .1 O b j e c t r e c o g n it io n in 2 D s c e n e s
S om e ex a m p les of a p p lic a tio n s involving 2D scenes are: re co g n itio n o f a lp h a n u m e ric c h a r
a c te rs from an im age of a d o c u m e n t, recognition of blood cells from an im age of a specim en
seen th ro u g h a m icroscope, an d iden tificatio n of houses a n d ro a d s fro m high a ltitu d e ae rial
p h o to g ra p h s. A g en eral fram ew o rk describing m a jo r tech n iq u e s used in o b je c t reco g n itio n
is show n in F ig u re 2.1. F e a tu re d e te c tio n tech n iq u e s a re used for d e te c tin g local fe a tu re s
such as edges (a t w hich th e g ray level changes a b r u p tly ), lines, curves, sp o ts, a n d co rn ers.
S e g m e n ta tio n p a r titio n s th e im age pixels in to h o m o geneous regions. B o th se g m e n ta tio n
th e pixels belong.
Recognition/Generic Description
^ M odel M atching/Object R ecognition
Relational structure
A Segm entation/Resegm entation Property Measurement
Scene Features
A Feature Detection
Image enhancement/restoration
Digitized Image o f the Scene
^ Imaging device
Real-World scene
t
Illumination
F ig u re 2.1: A n overview o f a ty p ical vision based a p p lic a tio n
R e se g m e n ta tio n tech n iq u e s g ro u p th e seg m en ted regions in th e im ag e in to g ro u p s
o r p a r ts t h a t satisfy c e rta in g eo m etric c o n s tra in ts . P ro p e r ty m e a su re m e n t a lg o rith m s
c o m p u te various p ro p e rtie s such as a rea, p e rim e te r, an d av erag e g ra y level, for such p a r ts .
M o d el m a tc h in g or o b je c t recognition is th e n re g a rd e d as id en tificatio n of im ag e p a r ts
t h a t c o rre sp o n d to th e o b je c t p a r ts and satisfy th e a p p r o p ria te c o n s tra in ts .
2 .2 .2 F e a tu r e D e t e c t io n
W e d esc rib e basic fe a tu re d e te c tio n tech n iq u es used for d e te c tin g v ario u s local fe a tu re s in
th e im age.
1. T e m p la tin g
A su b im ag e of a local fe a tu re t h a t is to be d e te c te d is re g a rd e d as a te m p la te an d
m a tc h e d a t every possible po sitio n in th e im age for b e st fit. T h e deg ree o f m a tc h or
m ism a tc h identifies th e fe a tu re a t th e c o rre sp o n d in g pixels. T h u s if an d t ( z , j )
re p re se n t pixel in ten sitie s in th e im age an d th e te m p la te , respectively, a m e a su re of
th e m ism a tc h betw een th em can be expressed by (R osenfeld, 1988) D (a,6)(/)^} =
re la tiv e to t h a t of /. T h e value of (a, b) t h a t m inim izes D re p re se n ts th e m o st likely
p o sitio n o f th e te m p la te in th e im age. T h is m e th o d is c o m p u ta tio n a lly in ten siv e an d
does n o t give c o rre c t re su lts if th e im age in te n sity varies sign ifican tly over a re a s of
th e size of th e te m p la te .
2. E d g e d e te c tio n
E d g e d e te c tio n tech n iq u es a tte m p t to find pixels t h a t lie on th e b o rd e rs betw een
d ifferen t o b je c ts in th e im age. Som e s ta n d a r d a p p ro a c h e s used a re (R osenfeld, 1988):
• M ask m atch in g : A te m p la te re p resen tin g ideal edges in v ario u s o rie n ta tio n s is
m a tc h e d in th e n eig h b o rh o o d o f each pixel in th e im age. A pixel is classified as
an edge pixel if th e degree o f such a m a tc h is sufficiently high. S h a rp m a tc h e s
are o b ta in e d by using m asks which are second differences of ideal s te p (or ra m p )
edges. T h is tech n iq u e is also used for d e te c tin g lines, curves, s p o ts a n d co rn ers.
• G ra d ie n t m ag n itu d e: If Aa; an d A y d e n o te th e first differences o f th e im age g ra y
level in th e x and y d irectio n s, th e n th e d ire c tio n of m ax im u m r a te o f ch an g e of
g ray level is ta n ~ ^ [ A y / A ^ ) ^ and th e g ra d ie n t m a g n itu d e o f th is m ax im u m ra te
of ch an g e is s q r t [ A l + A ^). A pixel lies on an edge if th e g ra d ie n t m a g n itu d e
a t t h a t pixel is sufficiently high. T h e differences A a; an d A y can be re g a rd e d
as good a p p ro x im a tio n s to th e p a rtia l d eriv a tiv e s a n d th e d ig ita l im age as a
good a p p ro x im a tio n to a sm o o th ly vary in g b rig h tn e ss fu n c tio n . T h e g ra d ie n t
m a g n itu d e ap p ro ach has several re fin em en ts (e.g. local m a x im u m selection,
differences of averages, etc.) to overcom e th e effect of noise in th e im age. A
d e ta ile d d esc rip tio n of th e se can be fo u n d in (R osenfeld, 1988).
• L ap la cian Z ero-crossing
In th is a p p ro a c h , a L aplacian of th e im ag e g ra y level i.e. th e su m o f th e second
differences in th e x an d y d irec tio n s in th e n eig h b o rh o o d o f a given pixel is
c o m p u te d . T h is sum is positive on one side of an edge a n d n eg a tiv e on th e
o th e r, hence its zero-crossings define th e lo c a tio n of th e edges.
• H ough tra n sfo rm s
T h e H ough tra n sfo rm a tte m p ts to d e te c t fe a tu re s such as lines, circles, or
curves, t h a t have e q u a tio n s of a p a rtic u la r ty p e by w orking in a s u ita b le p a
