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R o b o t V i s i o nY . S h i r a iElectro technica l Labo ratory , 1 -1-4 , Um ezono , Sakura-mura , N i ihar igun , Ibaraki , JapanRobot vis ion is widely s tudied in Ja pa n to real ize f lexiblem anu factu ring systems. The study of robot vis ion includesthe development o f the fo l lowing themes: input devices forrange information, featu re extract ion fo r inspect ion orposi tioning, high-speed image processors, s tereo vis ion,three-dimensional shape recovery, object recognit ion bymodel matching, and so forth. This pap er describes someinteresting work in these ields.1 . I n t r o d u c t i o nMany industrial robots have recently been used invarious fields, and strenuous efforts are beingdevoted to the research a nd develo pment of practicalrobots. In the industrial world, the working condi-tion of robots is well prepared for conventionalindustrial robots that can work without sensors orintelligence. In orde r to realize flexible manufac tur-ing systems, more advanced robots are needed thatcan adapt to new situations easily. One of the mostimportant techniques for such flexible robots is theability to understand the environment of a robotthrough visual information.Robot vision is not necessarily used together withmanipulators. It is also used, for example, forinspection or for bonding of IC circuits. The mainadvantage of robot vision is that the output isimmediate feedback to the environment. In inspec-tion, for example, objects with defects are removed.Thu s robot vision can work on-line in contrast to theoff-line image analysis such as processing of remo tesensing data or medical images.The study of robot vision includes the develo pmentof the following themes: input devices, featureextraction, high-speed image processors, three-di-mensional shape recovery, model matching, and soforth. This paper describes recent research anddevelopment in these areas.

2 . I n p u t D e v i c e sThe most popular input device is an imagi ng deviceused fo r television cameras. In this section, however,

conventional imaging devices are excluded becausethese devices are not proper to robotics. Proximitysensors and range sensors are described in thefollowing articles.

2.1. Prox imi ty SensorsProximity sensors are used for manipulation orlocomotion to detect three-dimensional positions ofknown objects close to robots, or to avoid collisions.This article introduces two proximity sensors formanipulation.One is a new optical proximity sensor being devel-oped for trajectory control of manipula tors [29]. Thetrajectory is def ine d as a cross line of two planes. Thisis a simplified model of the trajectory that isdete rmine d by the shape of objects, for example, inthe case of welding or seaming.The sensor consists of thirty infrared LED's con-structing a circular structured light, as shown inFigure 1, and a position sensitive detector (PSD).Lens 1 in the figure converges the LED beamsconically at a focal point on the PSD axis. The beamsare projected onto an object surface. Lens 2 forms

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F ig . 2 . F o u r p o i n t i ma g e s o n P S D.t h e i m a g e o f t h e l i g h t s p o t o n t o P S D . L E D ' s a r es e q u e n ti a ll y t u r n e d o n a n d o f f so th a t o n l y o n e L E Dis t u r n e d o n a t a n y t i m e . T h e P S D i s a P - N j u n c t i o ns e m i c o n d u c t o r i n w h i c h t h e P l a y e r is f o r m e d b y ah o m o g e n e o u s r e s i s t a n c e l a y e r . I t c a n d e t e c t t h ep o s i t i o n o f a l i g h t s p o t a s a q u an t i t y o f e l ec t r i cc u r r e n t . T h e d e t e c t e d p o s i t i o n o f t h i r ty L E D ' s im a g eco n s t i t u t e s a c l o s ed cu rv e .S i n c e t h e d i r e c t i o n o f e a c h L E D ' s li g h t b e a m is

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k n o w n , t h e t h r e e - d i m e n s i o n a l p o s i t i o n o f t h e li g h ts p o t o n t h e s u r f a c e c a n b e m e a s u r e d b y t ri a n g u la t io nf r o m t h e p o s i t io n o f th e s p o t o n t h e P S D . T h ep r o x i m i t y s e n s i n g s y s t e m d e t e r m i n e s f i r s t t h e i n t e r -s e c t i o n s o f t h e t r a j e c t o r y w i t h t h e c l o s e d c u r v ec o m p o s e d b y d e t e c t e d s p o ts , a n d t h e n c o m p u t e s t h ee q u a t i o n s o f tw o p l an e s . T h i s p r o b l e m is r e d u c e d t of i n d i n g t h e f o u r p o i n t s o n t h e c l o s e d c u r v e , a s s h o w ni n F i g u re 2 (P l an d P2 d e n o t e i n t e r s ec t i o n s , an d P3 an dP4 d e n o t e t h e t h i r d p o i n t s t o c o m p u t e p l a n e e q u a -t i o n s , r e s p ec t i v e l y ) .A n o t h e r o p t i c a l p r o x i m i t y s e n s o r i s b e i n g d e v e l o p e df o r d e t e c t i n g t h e p o s i t i o n a n d t h e o r i e n t a t i o n o f as u r f a ce [22 ]. T h e s en s o r co n s i s ts o f s i x L E D 's , p l aceda s s h o w n i n F i g u r e 3 , a n d t w o p h o t o t r a n s i st o r s i n t h ec e n t e r . O n e o f t h e p h o t o t r a n s i s to r s , C , h a s a n a r r o wf i e ld o f v i s io n a n d is u s e d t o m e a s u r e t h e l i g h ti n t e n si t y o f d i f f u s e r e f l e c t io n , w h i le t h e o t h e r o n e ,C ' , w i t h a w i d e f i e l d o f v is io n , m e a s u r e s m o s t l y t h el i g h t i n t en s i t y o f s p ecu l a r r e f l ec t i o n .I n o r d e r t o m e a s u r e t h e d i s t a n c e t o t h e s u r f a c e ( P inF i g u re 3 ) , t h e l i g h t i n t en s i t y o f L E D A an d A ' ism o d u l a t e d w i t h s in t o t, a n d t h e l i g h t i n t e n s it y o f Ba n d B ' w i th c o s to t. T h e l ig h t e m i t t e d f r o m A o r A ' a n dd i f f u s e l y r e f l e c t e d a t P is r e p r e s e n t e d b y t h e f o ll o w -i n g e q u a t i o n .

zLA - sin tot. (1)(a ~ + z2)3'~T h e l i g h t f ro m B o r B ' i s s i m i l a r l y g i v en b yL ~ - s in tot (2 )(b~ + z~)3'~T h e t o t a l o f l i g h t in t en s i t y d u e t o A , A ' , B , an d B ' isg i v e n b y

.Lr = c sin tot + 0T, (3)w h e r e t h e a m p l i t u d e c is d e t e r m i n e d b y e q u a ti o n s ( 2)a n d ( 3) , a n d t h e r e f l e c t iv i t y o f t h e s u r f a c e . T h e p h a s esh i f t 0 r i s g iven by

( a 2 + z 2 1 ~ J20v = t a n -~ - - (4)\b 2 + z21T h u s , i f t h e p h a s e s h i f t 0 x is m e a s u r e d ( b y t h e p h o t ot r a n s i s to r C ) , th e d i s t a n c e z c a n b e o b t a i n e d b y s o lv i n gE q u a t i o n (4 ) .I f l ig h t is e m i t t e d f r o m A a n d B , a n d s p e c u l a r l yr e f l e c t i o n is d e t e c t e d ( b y p h o t o t r a n s i s t o r T ' ) t h ep h as e s h i f t i s s i m i l a r l y g i v en b y t h e fo l l o w i n ge q u a t i o n .

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W e c a n se l ec t a s u i t a b le p h o t o t r a n s i s to r d e p e n d i n go n t h e p r o p e r t y o f t h e s u r fa c e .I n o r d e r t o m e a s u r e t h e o r i e n t a t i o n o f a s u r f a c e , ap a i r o f L E D ' s , B a n d B ' , a n d C e n C ' , a r e s e q u e n t i a l l yu s e d . L e t 8 d e n o t e t h e r o t a t i o n o f t h e s u r f a c e a b o u t

t h e y - a x is , a s s h o w n i n F i g u r e 4 . ' T h e a n g l e 8 i so b t a i n e d b y L E D B a n d B ' m o d u l a t e d b y si n cot a n dc o s to t, r e s p e c t i v e l y . T h e p h a s e s h i f t o f t h e d i f f u s e l yr e f l e c t e d l i g h t a t P i s g i v e n b y0~ = tan- ' - (6)z + b t a n 8 /T h e p h a s e s h i ft o f t h e s p e c u l a r l y r e f l e c t e d l i g h t o nt h e s u r f a c e i s g i v e n b y

( 4 _ z Z C O S S ( Z C O s S - b s i n S ) + b~ )O~ = tan -' (7)c o s S ( z c o s 8 + b s i n S ) +

T h e m e t h o d s d e s c r i b e d a b o v e m a k e u s e o f t h e p h a s es h i ft o f d e t e c t e d l i g ht . S i n c e t h e y n e e d n o t d e t e c t t h ep o s i t i o n o f t h e l i g h t s p o t s , t h e s y s t e m i s v e r y s i m p l e .O n e d i s a d v a n t a g e is t h a t t h e a c c u r a c y i s n o t h i g hb e c a u s e o f t h e l o w s e n s i ti v i ty o f t h e p h a s e s h i f t t o t h ec h a n g e o f t h e z v a l u e.

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2.2 . Range FinderT h e d i r ec t m e a s u r e m e n t o f r a n g e d a t a is v e r y u s e f u lf o r r o b o t v is io n . O n e o f t h e r a n g i n g m e t h o d s i s b a s e do n ac t i v e t r i an g u l a t i o n , i .e . , a p l an e o f l i g h t isp r o j e c t e d o n t o t h e o b je c t s f r o m o n e d i r e c t i o n , a n d ac a m e r a d e t e c t s i ts i m a g e f r o m a n o t h e r d i r e c t i o n [ 34 ].T h e m e t h o d h a s b e e n e x t e n d e d t o p r o j e c t in g m u l ti -p l e p l a n e s o f li g ht . W h i l e t h e p l a n e s o f l ig h t a r et u r n e d o n a n d o f f , t h e i m a g e s o f r e f l e c t e d li g h t a r em a d e . T h i s m e t h o d i s c a l le d ti m e c o d e d p a t t e r np r o j e c t i o n .T h e p r i nc i p le o f t h e t im e c o d e d p a t t e r n p r o j e c ti o nm e t h o d i s i l l u s t r a t e d i n F i g u r e 5 . S u p p o s e t h a tv e r ti c a l p l a n e s o f l ig h t a r e p r o j e c t e d a n d t h a t ac a m e r a i s p l a c e d t o t h e r i g h t o f t h e l ig h t s o u r c e . L e t Nd e n o t e t h e r e s o l u t i o n o f t h e v e rt i ca l p a t t e r n , a n d l e t nd e n o t e t h e n u m b e r o f im a g e s t a k e n b y t h e c a m e r a .E a c h v e r t i ca l p l a n e i s c o d e d b y n o n - o f f p a t t e r n s o fl ig h t p la n e s . T h e c o d i n g m a y b e b i n a r y o r e r r o rc o r r e c t i n g c o d i n g , d e p e n d i n g o n t h e s i t u a t i o n . T h ei n t e r s ec t i o n s o f a ll N v e r t i c a l p l an e s w i t h o b j ec ts u r f a c e s a r e s e q u e n t i a ll y o b s e r v e d a s n b i n a r y i m a g e s .E a c h v e r t ic a l p l a n e i s l o c a t e d b y d e c o d i n g n o b s e r v e di m a g es . T h e n , t h e r a n g e o f t h e c o r r e s p o n d i n g o b j e c ts u r f a c e s a r e o b t a i n e d b y t r i a n g u l a t i o n .M . M i n o u , T . K a n a d e , a n d T . S a k a i , [ 3 9 ] h a v ep r o p o s e d t h r e e a l g o r i th m s t o e x t r a ct i m a g e s o fp ro j ec t e d p l an es o f l i g h t (s li t i mag e ) . T h e f i r s t o n e( a l g o r i t h m A ) f i n d s e a c h c o d e d p a t t e r n s e q u e n t i a l l yu s i n g n b i n a r y i m a g e s . I t t a k e s t i m e p r o p o r t i o n a l t oN , a n d t a k e s l a r g e m e m o r y c a p a c i t y b e c a u s e e a c hi n p u t i m a g e i s p r o c e s s e d m a n y t i m e s u n ti l t h e N t hp l a n e i s f o u n d .A l g o r i t h m B e x t r a c t s t h e l o c a t i o n o f t h e s l it im a g e s i ne a c h i n p u t i m a g e . T h e r e s u l t s a r e a c c u m u l a t e d i n at w o - d i m e n s i on a l a r r a y c o r r e s p o n d i n g t o t h e i m a g e .A f t e r n i m a g e s a r e p r o c e s s e d , t h e a r r a y c o n t a i n s t h e

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c o d e a t e a c h p o s i ti o n . T h i s a l g o r i t h m is m u c h f a s t e rt h a n a l g o r i t h m A b e c a u s e i t p r o c e s s e s e a c h i n p u ti m a g e o n l y o n c e .A l g o r i t h m C is s i m i la r t o A l g o r i t h m B b u t i t e x a m i n e so n l y p o s s i b l e p o s i t i o n s o f s li t i mag es . I t f i r s t d ec i d esp o s s i b le l o c a t io n s o f s l it im a g e s b a s e d o n t h e p r e v i o u sr e s u l ts a n d t h e n d e c i d e s w h e t h e r t h e r e is a sl it i m a g eo r n o t . O f co u r s e , t h i s a l g o r i t h m i s t h e f a s t e s t o f a ll .F i g u r e 6 s h o w s e x a m p l e s o f a n e x p e r i m e n t w h e r e t h er e s u l t o f a p p l y i n g t h e t h r e e a l g o r i t h m s t o t h e s a m ei m a g e i s d i s p l a y e d . T h e r a t i o o f t h e p r o c e s s i n g s p e e do f a l g o r i t h m A , B , a n d C w a s 1 : 3 : 1 8 . T h e a b il it y toe x t r a c t t h e l o c a t io n s o f s li t i m a g e s w a s in t h e o r d e r o fa l g o r i t h m A , B , a n d C . T h e n o i s e r e m o v a l a b il it y w a sin t h e o r d e r o f a l g o r it h m A , C , a n d B .O n e o f t h e m o s t d i f f ic u l t p r o b le m s w i th t hi s m e t h o dis h o w t o p r o j e c t t i m e c o d e d p a t t e r n s o f s t r o n g l i g h tp l a n e s i n a s h o r t t i m e .3. Application of Image Processingto IndustryR o b o t vi s io n h a s b e e n u s e d i n i n d u s t r y f o r a s s e m b l y ,i n s p ec t i o n , c l a s s if i c a t io n , an d s o o n . T h es e s p ec i a lp u r p o s e s y s t e m s a r e s u i t a b l e f o r m a s s p r o d u c t i o n .N o w , e f f o r ts a r e b e i n g m a d e t o d e v e l o p m o r e f le x ib l ei m a g e p r o c e s s o r s f o r a v a r i e t y o f a p p l i c a ti o n f ie l d s.T h i s s e c t i o n d e s c r i b e s s o m e p r a c t i c a l v i si o n s ys t e m sa n d i n t r o d u c e s r e s e a r c h w o r k f o r n e w a p p l i c a t i o n s .

3.1 . Inspection of ProductA f l e x ib l e v i s io n s y s te m h a s b e e n d e v e l o p e d a n d u s e df o r i n s p e c t i o n o f s m a l l o b j e c ts o n a b e l t c o n v e y o r [ 2 6 ].T h i s s y s te m , c a l l e d " M u l t i -W i n d o w " , c o n s is t s m a i n l yo f a t w o - d i m e n s i o n a l s o li d st a t e c a m e r a , a n i m a g ea n a l y z e r , a n d m a n - m a c h i n e i n t e r f a c e s a s s h o w n i nF i g u r e 7 . I n o r d e r t o t a k e a n im a g e o f m o v i n g o b j e c t s,

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F i g . 6 . R e s u l ts o f sl i t i m a g e d e t e c t i o n w i t h 9 b i t e r r o r c o r r e c t i n g c o d e . ( a ) A l g o r i t h m A . ( b) A l g o r i t h m B . ( c) A l g o r i t h m C .


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monitorTVFig. 7. Vision system use d for inspection.

a h i g h - s p e e d m e c h a n i c a l s h u t t e r i s a t t a c h e d t o t h ec a m e r a .T h e b l o c k d i a g r a m o f t h e a n a l y z e r is s h o w n i n F i g u r e8 . T h e a n a l y z e r i n c o r p o r a t e s f o u r t h r e s h o l d in gc i r c u i t c h a n n e l s , i n w h i c h t h e t h r e s h o l d c a n b ea d j u s t e d . O n e o f t h e s e c h a n n e ls is c o n n e c t e d t o af l o a ti n g t h r e s h o l d c ir c ui t. T h e o u t p u t b i n a r y i m a g e i sf u r t h e r p r o c e s s e d b y d i gi t al f i lt e r in g , i n w h i c hs m a l l e r p a t t e r n s a r e r e m o v e d a c c o r d i n g t o t h es p e c i f i e d c r i t e r io n .T h e p r i n c ip l e o f in s p e c t i o n is b a s e d o n t e m p l a t em a t c h i n g , i . e . m a t c h i n g a p a t t e r n i n a s p e c i f i e dr e g i o n ( w i n d o w ) to t h e t e m p l a t e . O n e o f t h e m a j o rf e a t u r e s is t h e a b i l it y t o s e t v a r i o u s k i n d s o f w i n d o w s ,s u c h a s c i r c l e s , a r b i t r a r y p o l y g o n s , a n d t h e i r d o u b l eo r t r ip l e lo o p s . C o m p l i c a t e d s h a p e s a r e c o m p o s e d o fa c o m b i n a t i o n o f t h o s e w i n d o w s . T e m p l a t e m a t c h i n gis p e r f o r m e d b y c o m p a r i n g t h e a r e a in t h e w i n d o ww i t h t h e t e m p l a t e .T h e s y s t e m h a s t h e a b i l i t y t o n o r m a l i z e t h e p o s i t i o no f a n o b j e c t b a s e d o n t h e c o o r d i n a t e s o f t h e t o p o rb o t t o m , a n d t h o s e o f th e l e f t a n d r i g h t e n d o f t h eo b j e c t i m a g e. F i g u r e 9 s h o w s e x a m p l e s o f w i n d o w su s e d f o r t y p i c a l a p p l ic a t i o n s .

3 .2 . C R T Disp lay In spec tion Syst emR e c e n t l y , m a n y C R T d i s p l a y s h a v e b e e n u s e d f o ro f f ic e a u t o m a t i o n e q u i p m e n t s . A u t o m a t i c v is u ali n s p e c t i o n o f t h e q u a l it y o f C R T d i s p l a y is r e q u i r e d t om a k e a n e f f i c i e n t p r o d u c t i o n l i n e a n d t o o b t a i n a no b j e c t iv e , q u a n t i t a t iv e i n s p e c t i o n r e s u l t . A C R Td i s p l a y i n s p e c t i o n s y s t e m h a s b e e n d e v e l o p e d a n du s e d f o r a n i n s p e c t i o n l i n e o f w o r d p r o c e s s o r s [ 4 3 ].A s a r e s u l t o f a n a l y z i n g a c o n v e n t i o n a l h u m a n v i su a li n s p e c t io n , t h e q u a l i t y o f t h e C R T d i s p l a y c a n b er e p r e s e n t e d b y fi v e i t e m s , a s s h o w n i n T a b l e 1 . T h et a b le a l so i n c lu d e s p r o p o s e d i n s p e c ti o n m e t h o d s f o rt h es e i t em s . S i n ce t h e q u a l i t a t i v e an a l y s i s o f t h eq u a l i t y o f fo cu s i n g was t h e m o s t d i f f i cu l t i t em , i t i sd e s c r i b e d a s a n e x a m p l e .I f a C R T d i s p l a y is o u t o f f o c u s , t h e c o n t r a s t o f as t r i p e p a t t e r n d e c r e a s e s . T h e c o n t r a s t , h o w e v e r ,d e p e n d s o n t h e c h a r a c te r is t ic s o f e a c h C R T . I n o r d e rt o a v o i d t h e e f f e c t o f t h e c h a r a c t e r i s ti c s , t h e c o n t r a s ti s n o r m a l i z e d b y a r e f e r e n c e p a t t e r n . F i g u r e 1 0 s h o w sa d i s p l a y p a t t e r n a n d t h e o b s e r v e d b r i g h t n e s s . T h ed i s p l a y p a t t e r n c o n t a i n s n a r r o w s t r i p e s ( P ) , a n d aw i d e a r e a ( P ') a s a r e f e r e n c e p a t t e r n . T h e s y s t e m

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Ic ~ " " r [e c i s i o n IC r i t e r i am e m o r yIK e y - b o a r d

!I P r i n t e ri n t e r f a c eIr " . . . . . . . .I F l o p p y d i s kII i n t e r f a ceII -I T r a n s m i s s i o n I'1a i n t e r f a c et

1 ri iII o-1 ri It . II II"7

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m ~ J IM i n i - f l o p p y Id i s k IIL . . . . . . . . . J- - ~ H o s t c o m p u t e r

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Fig. 8. Block diagram of analyzer.

I f l J I l l l l l i l l l l l l l

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" / / A" / / A

JFig. 9. Windows fo r inspection of label and level check fo r bottles.

Di lp lay Ppattern p ,

Gr a ylevelGraY evel between P a n d P '

Fig. 10. Determinat ion o f measure of focusing.d e t ec t s th e m i n i m u m a m p l i t u d e L a th a t r e fl e ct s th ef o c u s in g , a n d t h e m a x i m u m a m p l i t u d e L b t h a t isi n v a r i a n t to th e f o c u s i n g . T h e m e a s u r e o f f o c u s i n g isd e f i n e d a s :M f = 1 0 0 L a / L b . ( 9 )

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This method was compared with twenty humaninspectors and has proved to be practical.Th e o ther items can also be inspected using the samesystem. Table 1 shows the repetition accuracy foreach item excluding reflection. (It was not me asu redbecause there were no such samples). The inspection

Table 1CRT d isp lay qua l i t y inspection sys temI te m C r i t e r i a o f I n s p e c t i o n V a r i a n c ei n s p e c t i o n m e th o dF o c u s i n g D e f o c u s i n g G r a y l e v e l d i f f e re n c eo f g r a t i n g 2S c a n n i n g A b n o r m a l l y h i g h L i g h t i n te n s i t y a t d i f f e r -

i n te n s i t y e n t p a r t sa t t h e l e f t a n d r i g h to f t h e d i s p l a y a r e ae n d so f t h e d i s p l a y a r e aD i m e n s i o n D i s to r t i o n i n d i s - H e i g h t a n d w i d th o fp l a y t h e d i s p l a yD i m e n s i o n a n d a r e ad i s p l a c e m e n t

0 . 4 m m *

D i s to r t i o n R a s te r d i s to r t i o n M a x i m u m d i s t a n c e b e - _ + 0 . 3 m m *t w e e n d i s p l a y e dl i n e a n d l i n e w h i c h i ss u p p o s e d t o b es t r a i g h t e d g eL i n e a r i t y L a c k o f u n i f o r m i t y

o f d e f l e c t i o nA c tu a l d i s ta n c e s b e -tw e e n p a r a l l e l l i n e sw h i c h a r e s u p p o s e d t ob e e q u a l l y s p a c e d

- + 2 %

( * 6 i n c h e s C R T)

Fig . 1 1 . CR T d i sp l ay i nsp ec t i on sys t em.

is performed in two stages: one for local patternanalysis, such as focusing and reflection, the otherfor global patter n analysis. In each stage, input imageis made by a small TV camera mounted on a robotmani pula tor as shown in Figure 11, so that the systemis able to handle different kinds of displays in thesame inspection line.

Fig. 12. Pile of crankshafts.

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[ 9 1F i g . 1 3 . R o u g h s k e t c h o f a c r a n k s h a f t .

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3.3 . Recogni t ion o f Pi led C rankshaf tsMo s t i n d u s t r i a l v i s i o n s y s t ems d ea l w i t h an i s o l a t edo b j e c t . T h e r e h a s , h o w e v e r , b e e n s o m e r e s e a r c hw o r k d o n e o n t h e r e c o g n i t io n o f m u l t ip l e o b j e c tst o u c h i n g t o g e t h e r . T h e m o s t d i ff i c u l t c a s e is t h e o n ew h e r e o b j e c t s o b s t r u c t e a c h a n o t h e r . A t y pi c a le x a m p l e o f t h i s c a s e i s t h e r e c o g n i t i o n o f p i l e dc ran k s h a f t s [ 4 ] .I n t h e s t u d y i t i s a s s u m e d t h a t c r a n k s h a f t s a r e p i l e du p t o t w o l ay e r s , an d t h a t t h ey a r e i n a s t ab l e s t a t e .F i g u r e 1 2 s h o w s a n e x a m p l e o f a p i le o f c r a n k s h a f t s .E a c h c r a n k s h a f t i s r o u g h l y s k e t c h e d a s s h o w n i nF i g u r e 1 3 . I t h a s n i n e c y l i n d r i c a l c o m p o n e n t s ( 1 - 9 )a n d b o a r d j u n c t i o n s b e t w e e n c y l in d e r s. T h e i m a g e o fc o m p o n e n t s 1 a n d 5 is m o d e l e d b y a t r a p e z o i d a n dt h a t o f t h e o t h e r c o m p o n e n t s i s m o d e l e d b y ar e c t a n g l e . T h e c r a n k s h a f t is m o d e l l e d i n te r m s o f t h es h a p e a n d t h e s iz e o f c o m p o n e n t s , d i s t an c e s b e t w e e nc o m p o n e n t s , o r i e n ta t io n s , a n d s o o n .T h e i n p u t is a b i n a r y i m a g e a s s h o w n i n F i g u r e 1 4.S i n c e c r a n k s h a f t s c o n t a i n m a n y c o m p o n e n t s o fr o u n d c o r n e r s , m o s t o f t h e r e g io n s c o r r e s p o n d i n g t ot h e c o m p o n e n t s a r e s e p a r a t e d f a ir ly w e ll f r o m e a c ho t h e r w h e n t h e y a r e i l l u m i n a t e d f r o m t h e s a m ed i r e o " w i th a c a m e r a . E a c h c o n n e c t e d r e g i o n is

f i rs t o b t a i n e d b y tr a c k i n g t h e b o r d e r , a n d t h e n l in es e g m e n t s a r e f i t t e d t o t h e b o u n d a r y p o i n t s . T h ef o l l o w i n g r e c o g n i t i o n p r o c e s s is b a s e d o n t h e m o d e lo f t h e c r a n k s h a f t.I t is e x p e c t e d t h a t r e g i o n s c o r r e s p o n d i n g t o t h ec o m p o n e n t s 1 -5 m a y b e w e l l e x t r a c t e d b e c a u s e t h e ya r e se p a r a t e d f r o m o t h e r c o m p o n e n t s. A m o n g t h e m ,r e g i on s o f c o m p o n e n t s 2 - 4 a r e in g o o d s h a p e( r e c ta n g l e ) . T h e r e f o r e t h e c o m p o n e n t 4 ( o r 2 ) is f i rs ts e a r c h e d f o r . T h e n t h e c o m p o n e n t s 3 , 2 ( o r 4 ) , 1 ,a n d , 5 a r e s e a r c h e d f o r i n t h i s o r d e r . T h e c o n s t r a i n tt h a t t h e i r ax es a r e o n a s t r a i g h t l i n e i s u s ed i n t h es e a r c h p r o c e s s . L a s t l y , t h e c o m p o n e n t s 6 - 9 a r eo b t a i n e d a n d u s e d t o d e t e r m i n e t h e d i re c t i on o f th ec r a n k s h a f t .F i g u r e 1 5 s h o w s c o m p o n e n t s o f a c r a n k s h a f t o b -t a i n e d b y a p p l y i n g t h i s p r o c e d u r e t o F i g u r e 1 4.A l t h o u g h t h e m e t h o d h a s n o t y e t b e e n p r ac ti c al lyu s e d , t h e e x p e r i m e n t s h o w s t h e p o s s i bi li ty o f r e c o g -n i z in g a p i le o f c o m p l i c a t e d o b j e c ts .

4 . I m a g e P r o c e s s o r sG e n e r a l l y , i m a g e p r o c e s s i n g r e q u i r e s m u c h c o m p u -t a ti o n b e c a u s e t h e a m o u n t o f in p u t d a t a is t r e m e n -d o u s . L o w l e v e l p r o c e s s i n g u s u a l l y r e p e a t s l o c a l

Fig. 15. Crankshaft obtained from F ig. 14.3 3 2 I F G C S N o r t h - H o l l a n d

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x l : x1 Lx j x( a )

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( b ) ( c )F i g. 1 6 . M a s k s f o r c o n t o u r e x t r a c t i o n . ( a ) M a s k f o r i n i t i a l c o n t o u re x t r a c t i o n . ( b ) M a s k f o r i s o l a t e d p o i n t e x t r a c t i o n . ( c) M a s k f o r

e x t r a c t i o n o f li n e w i t h w i d t h 1 .

operations at every pixel in the entire image. Robotvision should have a kind of a high-speed imageprocessing for on-line processing. This section de-scribes representative image processors developedrecently by Japa nese companies.4.1. Vis ion System o r Speci f ic Imag eProcessingThis chapter describes two vision systems for specificimage processing: for cont our extraction. Althoughthe functi on of the system is fixed, it can be used forvarious applications.The first system has been developed for extractingcontours o f objects and for labelling them accordingto a topological property [38]. The system firstconverts a video signal to a binary signal with afloating threshold. T he binary signal is filtered by the3 X 3 mask as shown in Figure 16(a) (variationscaused by rotations are omitted). Next, extra con-tours produced by noise are removed, i.e. isolatedcontours are re moved by the mask shown in Figure16(b), and contour s with width one are also remov edby the mask shown in Figure 16(c). Th e aboveprocessing is execu ted in parallel by special hardwa reat the video rate of 1/30 sec per image, and the resultis stored as a two-dimensional image in a specifiedmemory space of a computer.Th e rest of the processing is specified by a com pute rprogram and executed by a conventional computer.A contour point is located in the conto ur image in thememory.. When the contour point is found, thewhole contour is traced anticlockwise starting fromthe point. During the trace, the direction of thecontour is examined at each contour point. At eachcontour point, the direction code of the predecessorand that of the successor (as shown in Figure 17) areattached. This process is repeated until all contoursin the contour image have been traced. Figure 18shows an example of con tours , whe re Si (i = 1, 2, 3, 4)deno te starting points and arrows show the direction

3 2 14 Q 05 7

F i g . 1 7 . D i r e c t i o n c o d e .

of tracing. Each conto ur point is given the followingproperties: the coordi nate (x, y), the num ber repre -senting at which conto ur the point belongs (1, 2 .... ),the direction of predecessor B, and that of thesuccessor F.A topological relation between contours is analyzedusing direction codes of contour points. Let a"discriminant value" of each point be defined as

1 i f F - B > 0, (10)D = - 1 i f F - B < O .

Figure 19 shows the sign of D at each con tour point.Starting from Si, the con tour image is searched in theleft direction for contour points of ano ther contour.suppose the conto ur point of conto ur j Q" = i) isfound . If the D value is positive, cont our i is include dby cont our j. Otherwise, t he contou rs i and j areindependent. In the latter case, the search is contin-ued until a contou r with negative D value is foun d oruntil the point furthest left is reached.

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5 10 15 20"Fig. 19. D va lues of contours.

T h e w h o l e p r o c e s s i s e x e c u t e d i n l e s s t h a n a s e c o n df o r a n i m a g e i n c l u d i n g a b o u t 2 0 0 c o n t o u r p o i n t s .T h i s s y s t e m is a p p l ic a b l e t o s i m p l e i m a g e s w i t h h i g hc o n t r a s t o f b r i g h t n e s s b e t w e e n o b j e c t s a n d a b a c k-g r o u n d . A f u t u r e p l a n i nc l u d e s t h e d e v e l o p m e n t o f am o r e r e l i a b l e c o n t o u r e x t r a c t i o n m e t h o d .T h e s e c o n d s y s te m h a s b e e n d e v e l o p e d f o r e x tr a c tse d g e d f r o m a g r a y i m a g e a n d d e s c r i b e d a s l in e s [1 4].T h e s y s t e m is e x p e c t e d t o b e a p p l i e d t o a r o b o t v i s io nf o r l o c o m o t i o n . S i n c e i n d o o r s c e n e s u s u a l l y c o n t a i nm a n y l i n e s e g m e n t s , s u c h a s t h o s e c o r r e s p o n d i n g t ot h e e d g e o f c o r r i d o r s , d o o r s , s t a ir s , o r c o l u m n s ,

e x t r a c t i o n o f l in e s e g m e n t s i s v e r y i m p o r t a n t f o r p a t hf i n d i n g o r o b j e c t r e c o g n i t i o n .T h e i m ag e p ro ces s i n g s t a r t s w i t h s p a t i a l f i l t e r i n g t oe x t r a c t e d g e c a n d i d a t e s b y t h r e s h o l d i n g . T h i s o p e r a -t io n i s p i p e - l i n e p r o c e s s e d b y a n a r i t h m e t i c p r o c e s -s o r. T h e n , a th i n n i n g o p e r a t i o n is a p p l i e d t o t h eb i n a r y i m a g e t h u s o b t a i n e d . T h i s o p e r a t i o n is a l sop i p e - li n e p r o c e s s e d b y a lo g ic p r o c e s s o r .T h e t h i n n e d e d g e i m a g e i s t r a c e d a n d t h e e d g e s a r er e p r e s e n t e d b y c h a i n c o d e s. T h e c h a in o f e d g e s m a yb e s p l it i n to p i e c e w i s e l in e a r s e g m e n t s i f n e c e ss a r y ,an d a l i n e eq u a t i o n i s f i t t ed t o each l i n e s eg m en t .T h e s e l og ic s a r e i n p l e m e n t e d b y sp e c ia l h a r d w a r ec i rc u i ts . T h e r e s u l t is s t o r e d i n a ta b l e m e m o r y s o t h a ti t m a y b e a c c e s s e d b y a n e x t e r n a l b u s l i ne .F i g u r e 2 0 ( a ) a n d ( b) s h o w s a n e x a m p l e o f a n i n p u ti m a g e a n d e x t r a c t e d l in e s , r e s p e c t iv e l y . T h e s c e n e isn o t u n i f o r m l y i l l u m i n a t e d ; i t is i l l u m i n a t e d b yf l u o r e s c e n t la m p s f r o m a b o v e a n d b y n a tu r a l l i g htf r o m t h e r i g h t s i de . T h e r e f o r e , o b j e c t s a r e n o tl o c a t e d b y s i m p l e t h r e s h o l d i n g . T h e p r o c e s s i n g t im eis a b o u t 2 0 0 m s e c . w h i c h is m u c h f a s t e r t h a n w i t hc o n v e n t i o n a l i m a g e p r o c e s s o r s . A l t h o u g h t h e f u n c -t i o n o f t h e s y s t em i s l i m i t ed , t h e wh o l e s y s t em i sc o m p a c t e n o u g h f o r a m o b i l e r o b o t vi si o n.

4.2. Flexible Image ProcessorsT h e r e a r e t w o m a i n a p p r o a c h e s t o m a k i n g im a g ep r o c e s s o r s m o r e f l e x i b l e . O n e i s t o m a k e a s y s t e mt h a t c o n s is t s o f m a n y s p e c ia l p u r p o s e c i rc u i ts , a n d t h eo t h e r i s t o p r o v i d e i m a g e p r o c e s s i n g m o d u l e s w i t hw h i c h u s e r s c a n b u i l d a s u i t a b l e s y s t e m .T h e v i s i o n s y s t e m " T O S P I X " [ 2 0 ] b e l o n g s t o t h ef o r m e r c a t e g o r y . T h i s p r o d u c t is a n e x t e n s i o n o f

Fig. 20. Exam ple of processing. (a) Input image. (b) Extracted lines.3 3 4 1 F G C S N o r t h - H o l l a n d

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" Lo ca l Pa r a l l e l Pa t t e r n P r o cesso r ( PPP) " [ 3 5 ] . Ano v e r a l l b l o c k d i a g r a m i s d e p i c t e d i n F i g u r e 2 1 . T h ei m a g e m e m o r y c o n s is t s o f f o u r l a y e r s o f 5 1 2 x 5 1 2p i x e l s w i t h e i g h t b i t s p e r p i x e l . T h e d a t a m e m o r ys t o r i n g f o u r - l i n e i m a g e d a t a e n a b l e s p a r a l l e l a cc e ss -i n g o f 4 x 4 i m a g e d a t a f o r l o c a l p a r a l l el o p e r a t i o n s ,s u c h a s t w o - d i m e n s i o n a l c o n v o l u t i o n , m i n i m u m /m a x i m u m / m e d i a n d e t e c t i o n , l o g i c a l f i l t e r i n g , a n dr e g i o n l a b e l i n g . E x e c u t i o n t i m e s f o r t y p i c a l i m a g ep r o ces s in g o f a 5 1 2 x 5 1 2 im a g e a r e l i s t ed in Tab le 2 .A m u l t i - p u r p o s e i m a g e p r o c e s s i n g L S I c h ip h a s b e e nd e v e l o p e d [ 6 ] a s a m o d u l e f o r c o n s t r u c t i n g a v i s i o ns y s t e m . T h e p r o c e s s o r c a l l e d " I m a g e S i g n a l P r o c e s -so r ( I SP) " co n si s t s o f s ix u n i t s a s sh o w n in F ig u r e 2 2 .A d a t a u n i t t ra n s f e r s i m a g e d a t a t h r o u g h f o u r s h i ftr e g i s t e rs (S R ). A m e m o r y u n i t s t o r e s im a g e p r o c e s s -i n g o p e r a t o r s i n a 5 1 2 b i t R A M . A p r o c e s s o r u n i t

c o n s i s t s o f f o u r p r o c e s s o r e l e m e n t s ( P E ) . E a c h P Ee x e c u t e s p i x e l o p e r a t i o n s w i t h a n a r i t h m e t i c l o g i cu n i t ( A L U ) a n d a m u l t i p l i e r ( M U L ) . A l i n k a g e u n i ta p p l i e s a n o p e r a t i o n t o t h e f o u r o u t p u t s f r o m t h eP E ' s , a n d t h e n c o m b i n e s t h e r e s u l t w i t h a n o t h e ri m a g e d a t a i f n e c es s a r y. A n e v a l u a ti o n u n i t p e r f o r m st h r e s h o l d i n g o r a l o g i c a l o p e r a t i o n . A c o n t r o l u n i ts p e c i f i e s t h e f u n c t i o n o f e a c h u n i t a n d c o n t r o l s t h ed a ta f lo w.T h e h i g h s p e e d p r o s e s s i n g i s r e a l i z e d b y a p a r a ll e lp i p e l i n e m e c h a n i s m . T h e c y c le t i m e i s 1 67 n s e c ,w h i c h i s e n o u g h f o r r e a l t i m e p r o c e s s i n g o f a2 5 6 x 2 56 i m a g e ( 1 0 .9 m s e c p e r i m a g e ) . B y c o m b i n -i n g t h e p r o c e s s o r s , v a r i o u s i m a g e p r o c e s s e s a r ep e r f o r m e d . F o r e x a m p l e , 4 x 4 c o n v o l u t i o n c a n b ep e r f o r m e d b y f o u r p r o c es s o r s c o m b i n e d , a s s h o w n i nF ig u r e 2 3 .

( C O N T R O LC O M P U T E R )(1 /O B U S )

( D M A - B U S )

I N T E R F A C E

Lt M I C R O P R O C E S S O RS E Q U E N C E RI M IC R O P R O C E S S O R E M O R Y(4 K W O R D S )

( IX , I Y )( O X . O Y )( I M A G EM E M O R ~( I M A G ED A T A )

A L U

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DATAUSFig. 21. Block diagram of TOSPIX.

N o r t h - H o l l a n d F G C S I 3 35

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qq

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Fig. 23. Fou r ISP sys tem for 4 x 4 convolut ion3361 FGCS Nort h-Ho llan d

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I cOnUtPo~er

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ProcessorU n i tI r ~,(2) ]tan -' [(x - x v ) / ( y - y,)] - 0(x, y)[ < 00T h e c o n d i t io n (1 ) r e m o v e s e d g e s n e a r t h e v a n i s h in gp o i n t a n d ( 2) r e m o v e s l i n es w i t h i r r e l e v a n t d i r e c t i o n s .A n e w v a n is h i n g p o i n t is d e t e r m i n e d f r o m t h e e d g ep o i n t t h u s o b t a i n e d . T h i s p r o c e s s is r e p e a t e d u n t i lt h e v a n i s h i n g p o i n t b e c o m e s s ta b l e . F i g u r e 3 1 s h o w st h e r e s u l t o f t h i s m e t h o d .

5.4. Range F inding From KnownGeometryR a n g e i n f o r m a t i o n i s o b t a i n e d f r o m t h e i m a g e o fo b je c ts w i th k n o w n g e o m e t r y . T h i s m e t h o d is u s e f u lfo r v e r i f i c a t i o n v i s i o n , s u ch a s t h e p o s i t i o n i n g o f am o b i l e r o b o t , b e c a u s e t h e s h a p e a n d t h e p o s i t i o n o fo b j ec t s a r e p r e d i c t e d b e f o r e h a n d .A s im p l e e x p e r i m e n t w a s c a r r i e d o u t o n p o s i ti o n in gb y m e a s u r e m e n t o f a s p e c ia l m a r k [ 5] . A m a r k

F ig . 3 1 . E x t r a c t i o n o f v a n i s h i n g p o i n t , ( a) C a n d i d a t e v a n i s h i n gp o i n t . ( b ) F i n a l v a n i s h i n g p o i n t .

p a t t e r n , a s sh o w n i n F i g u r e 3 2 , is o b s e r v e d b y a T Vc a m e r a , a n d t h e v e r t i c e s a r e e x t r a c t e d i n t h e i m a g e .T h e t h r e e - d i m e n s i o n a l p o s i t i o n is e a s il y d e r i v e df r o m t h e le n g th A C a n d B D . T h e e x p e r i m e n t p r o v e dt h a t t h e p o s i t io n a n d t h e d i r e c t i o n o f th e c a m e r a a r eo b t a i n e d w i t h i n 5 % e r r o r s .A n o t h e r m e t h o d w a s p r o p o s e d f o r l oc a t in g a m o b i l er o b o t i n a r o o m [ 28 ]. T h e p r i n c i p l e o f th e m e t h o d i st o m a t c h t h e k n o w n p o s i t io n o f v e r ti c a l e d g e s ( m o d e le d g e s ) t o t h o s e d e t e c t e d i n a n i n p u t i m a g e . I t i sa s s u m e d t h a t v e r ti c a l e d g e s a r e e a s i ly o b t a i n e d i ni n d o o r s c e ne s . S in c e e x t r a e d g e s m a y b e d e t e c t e d , as i m p le m a t c h i n g m e t h o d d o e s n o t w o r k . T h e m a t c h -i n g u n i t i s a s e t o f f o u r e d g e s a s d e p i c t e d i n F i g u r e 3 3 .L e t ( x , y ) d e n o t e t h e p o s i t i o n o f a r o b o t P , a n d (x~,y,)d e n o t e t h e p o s i t io n o f a m o d e l e d g e E i ( fo r s i mp l i c it y ,E is a s s u m e d t o b e a t t h e o r i g i n o f t h e c o o r d i n a t es y s te m ) , a n d l e t 0~ d e n o t e t h e a n g l e b e t w e e n t h ed i r e c t i o n s o f e d g e s a s s h o w n i n t h e f i g u r e . T h ef o l l o w i n g e q u a t i o n h o l d s f o r i = 1 , 2 , 3 :x ( x , s , - y , c , ) + y ( x , c , + y , s ~ ) - ( x 2 + y 2 ) s , = O , (12)w h e re s~ = sin 0, an d c~ = co s 0,.S i n ce x , y , an d ad + y 2 a r e n o t z e ro , t h e d e t e r m i n a n t o ft h e s i m u l t a n e o u s e q u a t i o n i s z e r o .

~A

Fig. 32. Mark pattern: sq uare rotated 45 degrees.N o r t h - H o l l a n d F G C S I 3 ,t l

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/ I//t p

r~ E~

F i g . 3 3 . C o n f i g u r a t i o n o f e d g e s a n d r o b o t P

D =X l S l - - Y l ClXlCl -~ y ~ s l - s xXzS2 - - y2C2 X2C2 + y2s2 - s ~x3s3 - y3c3 x3c~ + y3s~ -s 3

= 0 . ( 1 3 )

If fo ur edges a re selected that satisfy Equation (13),the corresponding x and y are easily computed byEquation (12). However, the result is not reliablebecause the correspondence of four edges is notreliable. Since more edges are available, they must beused for the computation. The proposed methodestimates the position based on Bayesian rule inorder to get the most probable values from manyedges. The problem is formulat ed as maximizing theconditional probability funct ion P ( s , d ) , wher e s is theposition of the robot and d is the detected edges. Ifthe co rres ponde nce between model angles 0~ andobserved angles ~, is correct, the following funct ionmust be minimized:

kg = E( ,-03 ,j = l (14)

where k is the number of model edgee.If the estimated position of the robot is correct, thefollowing functions must be minimized:D i (0, .. .. . Ok), for i = 1 .. .. k-3, (15)where D i represe nts E quation (13) for each quarte tteof edges (there are k-3 quartettes).The problem is, then, further reduced, by La-grange's coefficient method, to minimization of the

following function:k-3

g ' = g + E X~D~. (16)i=1An experimental result for a natural room scene hasconfirmed the effectiveness of this method.

5.5. Shape rom TextureTexture is known to be a good clue for shaperecovery. The orien tati on of surfaces can be estimat-ed from so-called texture gradient: the change ofdensity and size of texture elements. Although thepossibility of shape recovery from texture has beendiscussed by many researchers, there have been fewworks that actually demonstrate the usefulness oftexture.The principle of the method o f shape recovery fromtexture is similar to that described in the previousarticle: to make use o f geometrical constraints in thescene. Th e simplest case is to use assumptions on theshape of textu re element . For exa mple, I keuchi [11 ]has been able to recover curved surfaces with circulartexture elements. The direction of a plane thatincludes can be computed from the shape in theimage (generally elliptic) by the same method asdescribed previously.A surface orientation can also be obtained fromvirtual parallel lines extracted from a texture [32].Figure 34 shows a simple example of a texture withcircular patterns. Since the image is formed by aperspective trans formation, the shape o f the textureelements is deformed. A vanishing point is obtainedby a pair of elements. It is on the line passing throu gh

Fig. 34. Circular texuture elements and vanishing points.342 I FGCS Nort h-Hol land

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V a n i s h i n gp o i n t

d

Fig. 35. Vanishing point from a pair o f texure elements.t h e m a s s c e n t e r o f t h e e l e m e n t s . L e t d a n d d~ d e n o t et h e d i s ta n c e f r o m t h e v a n i s h i n g t o t h e m a s s c e n t e r o ft h e e l e m e n t s o f t h e p a i r a s s h o w n i n F i g u r e 3 5. T h e i rr a ti o c a n b e c o m p u t e d f r o m t h e a r e a o f th e t w oe l e m e n t s S ~ a n d $ 2 .

d 3 1 /d 3 ~ = S , / $ 2I n F i g u r e 3 4 , v a n i s h i n g p o i n t s o b t a i n e d b y 1 5 1 5 p a i r so f v i r tu a l p a r a l l e l l in e s a r e p l o t t e d . I f a s t r a i g h t l i n e isf i t te d t o th o s e p o i n t s , t h e s u r f a c e n o r m a l is d e r i v e dp r e c i s e l y f r o m t h e l i n e .6 . Shape f rom I l l umi nat i onW e c a n i n f e r sh a p e i n f o r m a t i o n f r o m a m o n o c u l a ri m a g e i f t h e i l l u m i n a t i o n a n d t h e s u r f a c e p r o p e r t i e sa r e k n o w n [8 ]. I f m u l t i p l e i m a g e s a r e a v a i l a b l e u n d e rd i f f e r e n t i l l u m i n a t i o n c o n d i t i o n s , s u r f a c e o r i e n t a -t i o n s a t a n y p o i n t i n t h e i m a g e c a n b e o b t a i n e d . T h i ss e c t i o n i n t r o d u c e s a f e w t y p i c a l m e t h o d s .6.1. Three-Dimensional MeasurementUsing ShadowsA v is io n s y st e m h a s b e e n p r o p o s e d t o m e a s u r e t h et h r e e - d i m e n s i o n a l ( 3- D ) p o s i t io n s o f w i r e t i p s [ 3].T h e s y s t e m o b s e r v e s l i n e - l i k e o b j e c t s a b o v e t h ew o r k i n g t a b le w i t h a k n o w n p o i n t l i g h t s o u r c ei l l u m i n a t i o n s , a s s h o w n i n F i g u r e 3 6 , w h e r e l i n e -l i k eo b je c ts A B a n d C D , a n d t h e s h a d o w s A B a n d C D a r eo b s e r v e d i n t h e i n p u t i m a g e .T h e p r i n c ip l e o f 3 - D m e a s u r e m e n t is e a si ly ex -

T h e i m a g e p l a n e o f " l ' V -c a m e r a

Po i n t L i g h t So u r cePSProject ing

T h e l e n s f o c u sLD

T h e

= Y

sh ad o w Wo r k i n g tab l e

Fig. 36. Principle of measurement.N o r t h - H o l l a n d F G C S I 3 ! 3

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p l a i n e d u s i n g F i g u r e 3 6 . S i n c e th e p o s i t i o n o f th ew o r k i n g t a b l e a n d t h e l e n s f o c u s a r e k n o w n , t h e 3 - Dp o s i t io n o f s h a d o w ( A ) is o b t a i n e d f r o m t h e i m a g e o fs h a d o w ( A ') . T h e 3 - D p o s i t io n o f t h e t ip o f t h e l i ne -l ik e o b j e c t is d e t e r m i n e d a s th e i n t e r s e c ti o n o f t h e l in ep a s s i n g t h r o u g h P S a n d s h a d o w ( A ) w i t h th e l in ep a s s i n g th r o u g h L D a n d t h e i m a g e p o i n t ( A ') .T h u s t h e d e t e r m i n a t i o n o f t h e 3 -D p o s i ti o n isr e d u c e d t o th e d e t e r m i n a t i o n o f t h e t ip o f t h e li ne -l ik e o b j e c t a n d t h e m a t c h i n g s h a d o w t ip s in t h ei m a g e . T h e m a t c h i n g m e t h o d is b a s e d o n th ef o l l o w i n g c o n s t r a i n t. T h e l i n e p a s s in g t h r o u g h t h et i p a n d t h e s h a d o w m u s t p a s s t h e l i g h t s o u r c e P S .T h e r e f o r e , i n th e i m a g e p l a n e , a l l p a i r s o f a ti p a n dt h e m a t c h i n g s h a d o w m u s t a l so p a ss t h e i m a g e o f t h el i g h t s o u rce . S i n ce t h e p o s i t i o n o f t h e l i g h t i n t h ei m a g e i s k n o w n , t h i s l i n e i s u n i q u e l y d e t e r m i n e d( d o t t e d l i n e s i n t h e F i g u r e 3 6 ) . T h e s h a d o w t i p i ss e l e c t e d a m o n g t h e c a n d i d a t e s a s t h e o n e t h a t i s o nt h e li n e. F i g u r e 3 7 sh o w s a n e x a m p l e o f t h e m a t c h i n gp a i r s t h u s o b t a i n e d .I n t h e e x p e r i m e n t , w i r es a n d t h e i r s h a d o w s w e r eo b s e r v e d v e r t i c a l l y a b o v e t h e h o r i z o n t a l w o r k t a b l e .T h e i n p u t i m a g e ( a 2 5 6 b y 2 5 6 4 - b i t i m a g e ) w a sc o n v e r t e d t o a b i n a r y i m a g e b y t h re s h o l d i n g , a n ds t r in g s w e r e o b t a i n e d b y a p p l y i n g a t h i n n i n g o p e r a -t o r. T h e m e a s u r e m e n t e r r o r w a s l es s t h a n 1 .5 m m o r1% .6.2. M od ified Photometric StereoP h o t o m e t r i c s t e r e o is a w e ll k n o w n m e t h o d o fo b t a i n i n g s u r f a c e o r i e n t a t i o n b y c o n t r o l l i n g t h ei l lu m i n a t i o n d i r e c t i o n [ 4 2 ]. A m o d i f i e d m e t h o d h a sb e e n p r o p o s e d f o r a c c u r a t e ly o b t a in i n g t h e s h a p e o fan o b j ec t [ 1 6 ] . I n t h i s me t h o d , an i l l u mi n a t i o nc o n d i t i o n is c h a n g e d b y r o t a t i n g a n o b j e c t , a s s h o w ni n F i g u re 3 8 . Fo r s i mp l i c it y , t h e fo l l o w i n g co n s t r a i n t s

)

Fig. 37. Determ ination of pairs of tips and the shadows.

Lights o u r c e

Camera

I memoryMoni tor T.V .

Computer I I Floppy(HP 9845) disk

TurntableFig. 38. O bserving system.

a r e i m p o s e d :1 . A l i g h t s o u rce i s f a r f r o m an o b j ec t .2 . A c a m e r a is f a r f r o m a n o b j e c t .3 . A n o b j ec t i s co n v ex .4 . A n o b j e c t h a s p e r f e c t l y d i f f u s e s u r f a c e s , in w h i c ht h e r e f l ec t i o n i s u n i fo rm i n a l l v i ew er d i r ec t i o n s ,a n d t h e r e f l e c t a n c e is p r o p o r t i o n a l t o t h e c o s i n e o ft h e i n c i d e n t a n g l e .T h e o b s e r v e d l i g h t in t e n s it y o f a s u r f a c e i s f o r m u l a -t ed a s a f u n c t i o n o f t h e ro t a t i o n an g l e 13 o f t h et u r n t a b l e a n d t h e s u r f a c e n o r m a l w i t h r e s p e c t t o t h et u r n t a b l e .1 ( [ 3 ) = a + b co s [3 + c sin 13.

6

2

0 0 ~ " ' Fig. 39. Light intensity profile with respect to rotation angle.


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T h e s u r f a c e n o r m a l is d e r i v e d f r o m t h e c o e f fi c i en t sa , b , and c .F i g u r e 3 9 s h o w s a n e x a m p l e o f th e i n t e n s i ty p r o f i l ew i t h r e s p e c t t o t h e r o t a t i o n a n g l e . T h e c o e f f i c i e n t sa r e o b t a i n e d b y t h e le a st s q u a re s m e t h o d . A n

t o C o m p u t e r D i s t r i b u t e d S o u r c e s o fC i r c u l a r P o l a r i z a t i o n

Retarder ,-;X\X i ~ "t ~ \

~ . G l o s s 3 / O b j e c t s . . /

Fig . 40 . Schema t ic d iag ram of observ ing system.

e x p e r i m e n t a l r e s u l t s h o w e d t h a t t h e s u r f a c e n o r m a lo f a p o l y h e d r o n is o b t a i n e d w i th t h e a c c u r a c y o f o n ed e g r e e .

6.3 . Use O f Polarized LightP h o t o m e t r i c s te r e o m a y b e c o n v e n i e n t l y u s e d t oo b t a i n t h e n o r m a l o f d i f f u s e s u r f a c e s . G l o s s y s u r -f a c es , h o w e v e r , h a v e l it tl e s h a d i n g i n f o r m a t i o n , s i n cet h e y d o m i n a n t l y r e f l e c t s e c u l a r r a y s. I f g l o ss y su r -f a c es a r e c o m p l e t e l y s m o o t h l ik e a m i r r o r , t h e n t h e

s~

Fig. 41 . Geo me try o f observ in g system.

Fig. 42. Surface norm als obtained by polarized light. (a) A cylindrical can with painted picture.

\ ~ ~ ~ . . . . . . . . .

(b ) Surface normals .

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n o r m a l s m a y b e o b t a i n e d b y u s in g i l lu m i n a t i o n s o fk n o w n d i s t r i b u t i o n s [9, 3 1 ] . O t h e r g l o s s y s u r f ace ss u ch a s th o s e o f p l a s ti c s a r e v e ry d i f f i cu l t t o d ea l w i t hi n a c o n v e n t i o n a l w a y . A p o l a r i m e t r i c a p p r o a c h h a sb e e n p r o p o s e d f o r d e t e r m i n i n g s u r f a c e n o r m a l s o fs u ch g l o s s y s u r f aces [1 9 ] .T h e o b s e r v i n g s y s t e m is d e p i c t e d i n F i g u r e 4 0 . L e t u ss u p p o s e t h a t a s u r f a c e w i t h t h e n o r m a l v e c t o r n isi l l u m i n a t ed b y a c i r cu l a r l y p o l a r i zed l i g h t f ro md i r e c t i o n s , a n d t h e r e f l e c t e d l ig h t is o b s e r v e d f r o md i r e c t i o n v , a s s h o w n i n F i g u r e 4 1 . T h e s u r f a c en o r m a l n i s d e t e r m i n e d b y t h e i n c i d e n t a n g l e t ~ a n dt h e i n c l i n a t i o n an g l e ~ i n t h e f i g u re .E i g h t i m a g e s a r e t a k e n w i t h d i f f e r e n t a n g l e s o f t h er e t a r d e r . B r i g h t r e g i o n s i n e i th e r o f t h e e i g h t i m a g e sc o r r e s p o n d t o re g i o n s o f s p e c u l a r r e f le c t io n o n t h es u r f a c e . T h e p o l a r i z a t i o n p a r a m e t e r s a r e d e r i v e df ro m t h e l i g h t i n t en s i t i e s o f t h e e i g h t i m ag es [2] . T h er e la t io n s b e t w e e n t h e s e p a r a m e t e r s a n d t h e a n g le sa n d ~ a r e d e t e r m i n e d e x p e r i m e n t a ll y . T h u s t h es u r f a c e n o r m a l i s d e t e r m i n e d f r o m t h e e i g h t i m a ge s .F i g u r e 4 2 sh o w s a n e x a m p l e o f a n e x p e r i m e n t a lresu l t .T h e s h a p e o f t h e s u r f a c e i s a l so e s t i m a t e d f r o m t h ed i s t r i b u t io n o f s u r f a c e n o r m a l s i n a r e f le c t i n g r e g i o n[4 0]. F i g u r e 4 3 s h o w s a n e x a m p l e o f t h e d i s t r ib u t i o no f s u r f a c e n o r m a l s o b t a i n e d b y t h e p o l a r i m e t r icm e t h o d d e s c r i b e d a b o v e . S i n c e a r e f l e c t i n g r e g i o nm a y g e n e r a l l y c o n s i s t o f m u l t i p l e s u r f a c e s , t h e r e g i o nis f ir s t s e g m e n t e d i n t o s u r f a c e s b y c l u s te r i n g .T h e c l u s t e r i n g is b a s e d o n t h e h i s t o g r a m o f th es u r f a c e n o r m a l s . I n a c o n v e n t i o n a l m e t h o d , t h eh i s t o g ram w i t h r e s p ec t t o t~ an d ~0 m a y u s e d .H o w ev e r , t h e h i s t o g ram o f q~ is v e ry s en s i t i v e t o t h eo b s e r v a t i o n e r r o r i f t~ is s m a ll . T h e r e f o r e , t h e

j (II

1Fig. 43. Distribution of surfa ce normals in reflecting region.

h i s t o g ram is m a d e w i t h r e s p ec t t o q~ s i n t~ , i n s t ead o f~p, t o s u p p r e s s t h e e f f e c t o f t h e o b s e r v a t i o n e r r o r .S u r f a c e n o r m a l s a r e d i v i d e d a t t h e v a ll e ys o f t h eh i s t o g r a m s , a s s h o w n i n F i g u r e 4 4 ( a ) a n d ( b ) . T h ec o r r e s p o n d i n g s u r f a c e s a r e o b t a i n e d f r o m t h e t w oh i st o g ra m s . T h e f in a l s u r f a c e s a r e d e t e r m i n e d a st h e i r i n t e r s ec t i o n s , a s s h o w n i n F i g u re 4 4 ( c ) .F o r e a c h s u r f a c e , t h e s u r f a c e s h a p e is c l a ss i fi e d i n tot w o c a t e g o r i e s : p l a n e a n d c u r v e d s u r f a c e s w i t hg en e ra t i n g l i n es (s u ch a s cy l i n d e r ) . T h e c l a s s if i ca ti o nis b a s e d o n t h e s h a p e o f t h e r e f l e c t i n g r e g i o n , w h i c h i sd e s c ri b e d b y t h e s e c o n d o r d e r m o m e n t a b o u t t h ep r i n c i p a l ax i s a n d t h a t a b o u t t h e p e r p e n d i c u l a r a x is .I f t h e r a t i o o f t h e t w o m o m e n t s is sm a l l, t h e s u r f a c e isc l a ss i fi e d i nt o a c u r v e d s u r f a c e w i t h g e n e r a t i n g l in e s .O t h e rw i s e , t h e r a t i o i s c l o s e to o n e an d i t i s c l a s s if i edi n t o a p l an e .

( a ) A B C q t s i n q /

a

( b )

( c )

F ig . 4 4 . R e g i o n s e g m e n t a t i o n b y h i s t o g r a m o f tb a n d s i n + . ( a)H i s t o g r a m o f s u r f a c e n o m a l s w i t h r e s p e c t t o + s i n tb . ( b) H i s t o g r a mo f s u r f a c e n o m a l s w i t h r e s p e c t t o t ~ .

3 4 6 1 F G C S N o r t h - H o l l a n d

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( a )

( J ( c

Fig. 45. R ing operator. (a) Ra nge data on a circle. (b) Ring operator applied to various parts.

7 . R a n g e D a t a P r o c e s s i n gR a n g e d a t a o f a t h r e e - d im e n s i o n a l s c e n e c o n t a in r ic hi n f o r m a t i o n a b o u t t h e s tr u c t u r e o f a sc e n e . T h e r e a r et w o a p p r o a c h e s t o t h e e x t r a c t io n o f f e a t u r e s f r o mr a n g e d a t a: e d g e m e t h o d a n d r e g i o n m e t h o d . I n t h er e g i o n m e t h o d , r a n g e d a t a a r e d i v i d e d in t o sm a l ls u r f a c e e l e m e n t s , a n d t h e y a re m e r g e d t o u n i f o r mr e g i o n s o f p l a n e s o r c u r v e d s u r f a c e s [ 3 3 ]. I n t h e e d g em e t h o d , e d g e s o f s u r f a c e s a re f ir s t e x t r a c t e d a sf e a t u r e s o f t h e s c e n e [ 37 ] . S i n c e t h e e d g e m e t h o d isu s u a l l y m o r e e f f i c i e n t f o r s i m p l e s c e n e s , i t i s o f t e ni n v e s t i g a t e d f o r r o b o t v i s i o n .7.1. R ing Operator o r E dge Detectionfrom Range PictureM a t s u d a e t a l. [ 23 ] h a v e p r o p o s e d a r i n g o p e r a t o r f o re x t r a c ti o n o f e d g e s f r o m a r a n g e p i c t u r e ( e a ch p i x elo f th e r a n g e p i c t u r e h a s a r a n g e v a l u e ). T h e r i n go p e r a t o r , a p p l i e d t o a p o i n t , e x a m i n e s t h e r a n g ea l o n g a s m a l l c i r c l e c e n t e r e d a r o u n d t h e p o i n t , a ss h o w n i n F i g u r e 4 5 ( a ) . F i g u r e 4 5 ( b ) d e p i c t s r i n go p e r a t o r s a p p l i e d t o v a r io u s t y p e s o f p o i n ts i n t h er a n g e p i c t u r e . T h e p r o f i l e s o f r a n g e , w i t h r e s p e c t t ot h e a n g l e q~ o f t h e o p e r a t o r s , a r e i l l u s t r a t e d i n F i g u r e4 6 . T h e p r o f i l e is a p e r i o d i c f u n c t i o n w i t h t h e p e r i o d2 w . F e a t u r e s c a n b e e x t r a c t e d b y F o u r i e r a n a l y s i s o ft h e p r o f il e . T h e m a j o r c o m p o n e n t s o f .t he F o u r i e rt r a n s f o r m a r e i l lu s t r a t e d i n t h e f i g u r e . T h e f o l l o w i n gf a ct s c a n b e o b s e r v e d i n t h o s e c o m p o n e n t s .1. I n t h e j u m p e d g e , t h e fi r st a n d t h i r d c o m p o n e n t sa r e l a rg e .2 . I n th e p l a n e , fi rs t c o m p o n e n t s d e p e n d o n t h e

s u r f a c e o ri e n t a ti o n , w h i le t h e o t h e r c o m p o n e n t sa r e s ma l l .3 . I n t h e r o o f e d g e , t h e s e c o n d c o m p o n e n t is l a rg e ,w h i l e t h e f i r st a n d t h i r d c o m p o n e n t s a r e s m a l l.

4 . T h e + r o o f a n d - r o o f a re d i sc r i m i n a t e d b yc o m p a r i n g t h e r a n g e o f t h e c e n t e r w i th t h ea v e r a g e r a n g e a l o n g t h e r in g ( t h e 0 th c o m p o n e n to f th e F o u r i e r t r a n s f o r m ) .

T h e d i r e c ti o n o f t h e e d g e c a n a l s o b e d e r i v e d f r o mt h e p h a s e o f th e c o m p o n e n t s (q~ a n d q~ i n F i g u r e 4 6 ) .T h i s p r o c e d u r e c a n b e s c e en i n t h e f l ow c h a r t s h o w ni n F i g u r e 4 7 .A n e x p e r i m e n t u s i n g s y n t h e s i z e d r a n g e p i c t u r e sp r o v e s t h e e f f e c t iv e n e s s o f t h e o p e r a t o r . A l t h o u g ht h e c o m p u t a t i o n t i m e i s a b o u t t h r e e t i m e s a s l o n g a st h a t o f a n e d g e d e t e c t i o n u s i n g a S o b e l o p e r a t o r , t h er e s u l t c o n t a i n s m o r e u s e f u l i n f o r m a t i o n .

7.2. Robot Vision LanguageA r o b o t v i s i o n l a n g u a g e h a s b e e n d e v e l o p e d t od e s c r ib e f u n c t io n s r e q u i r e d f o r th e m a n i p u l a t i o n o ft h r e e - d i m e n s i o n a l o b j e c t s [ 25 ]. T h e g o a l o f t h er e s e a r c h w a s t o i m p l e m e n t a l a n g u a g e t h a t s a t i s f i e st h e f o l l o w i n g r e q u i r e m e n t s :1 . U s e r s n e e d n o t w o r r y a b o u t d e t a il s o f lo w e r - l e v e li m a g e p r o c e s s i n g .2 . I t m u s t b e a b l e to c o n s t r u c t o b j e c t m o d e l s t o b eu s e d f o r o b j e c t r e c o g n i t i o n .3 . I t m u s t b e a b l e t o d e a l w i t h t h r e e - d i m e n s i o n a li n f o r m a t i o n .4 . V i su a l d a t a a n d m o d e l s m u s t b e si m p l e e n o u g hf o r r e a l t i m e p r o c e s s i n g .T h e f i rs t v e r s i o n o f t h e l a n g u a g e c a l l e d " R V L / A "

N o r t h - H o l l a n d F G C S I 3 4 7

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R o bo t V i s i o n

AJUMP EDGERANGE

(a) W A VE F O R M ~ ~ ' ~ _0 ~P 27r

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Fig . 47. F lowchar t o f c la s s i fy ing rang e p ic tu re p ixe l .3 4 8 I F G C S N o r t h - H o l l a n d

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R o b o t Vision

~ 0 7 ~ ] ~ M a n ip ula tr'X ~Depth J ~ /

" ~ LpghtjPl?ne ~ E T VCameraHo'riz.

Fig. 48. Observing system for RVL/A.restricts visual data to one-dimensional range data,obtained by a system shown in Figure 48, where alight plane is projected on an object and the projecte dlight stripe is observed by a stereo pair of TVcameras. The current purpose is to provide usefulfunctions for verification vision.The language has three kinds of range data struc-tures. The first is a simple one-dimensional arraywhere range data along the stripe are stored. This iscalled the "range data profile". The second is afeature level expression derived from the rangeprofile data by segmentin g the profile into lines andcurves. This is called the "range profile list". Eachsegment is approximated by lines or arcs. Figure 49shows an exa mple o f the profil e list of a line segment.The third is the description of the shape and the sizeof the profile stored in the attribute list of LISP. Thisis the highest level description of visual data.The language supports the use of description storedin those data structures. It also provides users withthe defini tion of object models and a few functionsfor mani pulati ng the models. Object models are alsodescribed in the third level data structure. F o rexample, an e n g i n e c o v e r can be defined in terms of

the shape of the range profile. The languagesupports the access to the distance or the anglesb e t w e e n them.An experiment was done using RVL/A to demon-strate its effectiveness. The task is to assemble anexhaust and an engine. First, the engine model isdescribed with a few elements, such as an e n g i n ec o v e r and exhaust port (which are also defined in thethird level description). Once the model is defined,the control program is easy to write because it canmake use of the description of the models by higherlevel commands. Figure 50 shows the outline of theprogram, in which arm control is also included.8 . U s e o f T h r e e - D i m e n s i o n a lM o d e l sI n conventional vision systems, huma ns must supplythe procedures to recognize objects, i.e. we mustspecify which features must be e xtracted and how tomatch the m to object models. Ther e have been someapproaches to a vision system which is able tor e c o g n i z e objects if their models are defined in auniform way [1, 35]. Those approaches use three-dimensional (3-D) models because they provide acomplete description of t he shape of objects. T h e r eare, however, some problems in the use of 3-Dmodels. One is that it usually requires muc h e ffort to

~ o | Ell,m!"Ir4~l~2 IS3 I E4 05 h, 6 l e n t l th o f t h e l i n e?8 o r i s i n a l st r in 4 ~ n o9 f i t t i n l e r r or

NEXT I~NY-tan 0 *X * h

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Fig. 49. Range profile list of a line segment.( TAKRD )( PPRD )( F I ND E N GIN E )( MEASURE EN GIN E "PCENT )

, , .

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( MEASURE "ENGINE "P~AN E )...( MOVEARN X Y 2 A B C )

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; I N P U T P R O F I L E D A T A .; P R E P R O C E S S I N G ( S E G M E N T A T I O N ) .; V E R I F Y G A S O L I N E E N G I N E E X I S T E N C E .; ~ASUI~ ~]fl l~ POSITION OF T H E P R O F I L E .; IST EI.~T(POI~r) IS SELECTED.; DESTINATION OF HAND IS SE T.; ~A SIIRE ~ANE AX IS DIRECTION (OF POR T).; ROTATION OF WRIST IS SE T.; NO ~ AND FI T MUFFLER TO PORT.

F i g . 50 . O u t l i n e o f c o n t r o l p r o g r a m .

North- Holla nd FGCS I 349

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Robot Vision

construct such models. The other is how to matchvisual data to the models if the atti tude of the object isno t a p r i o r i known. These problems have beenstudied for realizing flexible robot vision [36].Objects for robot vision are mostly designed andmade by humans. There fore, the shape of objects isdescribed in the design process. In the CAD/CAMresearch, unified models are being developed to beused for design, structural analysis, and manufact ur-ing. If the model is used for robot vision, the modelbuilding efforts can be greatly reduced.In ou r vision system, 3-D models are man ipul ate d bya prog ram based on GEO MAP (GEOmetrical Model-ing and Processing) [15], a FORTRAN programpackage for such tasks as generation, synthesis,display, and kinematic analysis of 3-D bodies. Thegeometric model consists of basic bodies such aspolyhedra, a cylinder, and 2-1/2 objects, as shown inFigure 51. New bodies can be generated by binaryoperation s (such as union and intersection) of objectsalready defined. The original GEOMAP has beenmodified so that, for any views, all surfaces in thepredicted scenes can be precisely extracted in theimage.In orde r to solve the mat ching problem, hierarchicalmodels are used. In the first stage, higher-levelmodels are used to find promising candidates, andthen lower-level models are used to select a right oneamong the candidates. Those models are easily builtfrom geometric models represe nted by GEOMAP.

8.1. Reco gni t ion o f Glossy Objec tsAs described earlier in this paper, surface normals ofa glossy object can be obtained by observing polarizedlight reflected from the surfaces. Fig ure 52 shows anexample of surface normals of three regions derivedin this way [ 18]. Note tha t no t all surface normals areobtained by this method.

Fig. 51. Basic bodies in GEOMAP.

/ ii i

Fig. 52. Surface normals obtained by polarimetric method.i ! . . . . . i

L ! ! ! ! ~ i C .--'--'--"'-'--liii iFig. 53. Recogniuon result for Fig. 52.

Recognition of an unknown object is reduced to thematching of observed surface normals with objectmodels. Objects are assumed to be placed so that atleast one surface lies on a horizontal plane (table) in astable orientat ion. The matc hing process proceeds asfollows. First, candidate models are foun d, based onthe relative angles between observed surface nor-mals. The bottom face of the model is first selected asthe one that can actually lie on the table.Next, in the computer, the candidate models areplaced on the table so that observed directions ofsurface normals coincide with those of the models.The models are translated on the table so that the

( a ) ( b )

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o b s e r v e d p o s i ti o n s o f s u rf a c e n o r m a l s m a t c h i n th ei m a g e w i t h t h o s e o f th e m o d e l s . T h i s s t e p c h e c k s t h ed i s t a n c e b e t w e e n s u r f a c e s . N o t e t h a t w i t h o u t t h i sc h e c k , a s p h e r e m o d e l m a y m a t c h w i t h a n y d a t a .T h e f i rs t s t a g e o f t h e m a t c h i n g u s e s o n l y t h ed i r ec t io n o f th e s u r f a c e n o r m a l , a n d t h e s e c o n dm a t c h i n g u s e s th e s iz e i n f o r m a t i o n . T h u s , t h e u s e o ft h e h i e r a r c h i c a l m o d e l a t t a i n s e f f i c i e n t p e r f o r m a n c e .F i g u r e 5 3 s h o w s th e e x p e r i m e n t a l r e s u lt o f t h em a t c h i n g p r o c e s s a p p l i e d t o t h e s u r f a c e n o r m a l ss h o w n i n F i g u r e 5 2 .8.2. M atching An Extended GaussianImageA n E x t e n d e d G a u s s ia n I m a g e ( E G I ) w a s p r o p o s e d a sa 3- D o b j e c t r e p r e s e n t a t i o n [ 10 ]. T h e E G I o f a no b j e c t r e p r e s e n t s t h e d i s t r i b u t i o n o f t h e s u r f a c en o r m a l s . F i g u r e 5 4 il l u st r a te s a n e x a m p l e o f a n E G I ,w h e r e t h e a r r o w s i n (a ) s h o w t h e d i r e c t i o n o f t h es u r f a c e n o r m a l s , a n d t h e l e n g t h o f e a c h a r r o w i n ( b)s h o w s t h e a r e a o f t h e s u r f a c e s w i th t h e s a m ed i r e c t i o n .M o d e l s o f 3 - D o b je c t s c a n b e r e p r e s e n t e d b y t h e E G I .I f t h e s u r f a c e n o r m a l s o f a n o b j e c t in a sc e n e a r eo b t a i n e d , t h e o b j e c t i s a l s o r e p r e s e n t e d b y t h e E G I .T h e m a t c h i n g o f t h e E G I o f t h e o b j e ct w i t h th a t o f th em o d e l s i s m u c h e a s i er t h a n t h e m a t c h i n g o f t h e 3 -Dd a t a o f t h e o b j e c t w i th 3 - D m o d e l s [ 10 ].T h e u s e o f t h e E G I h a s b e e n p r o p o s e d a s a h i g h e rl e v el m o d e l d e s c r i p t i o n [ 12 ]. S u r f a c e n o r m a l s o f a no b j e c t c a n b e o b t a i n e d b y th e p h o t o m e t r i c s t e r e o

s y s t e m , as d e s c r i b e d e a r l i e r. A s e t o f s u r f a c e n o r m a l sp r o j e c t e d i n t h e i m a g e i s c a l l e d a n e e d l e m a p ( s e eF i g u r e 5 5 ) .T h e E G I i s e a si ly d e r i v e d f r o m t h e n e e d l e m a p . O nt h e o t h e r h a n d , t h e E G I is a l so d e r i v e d f r o m t h eo b je c t m o d e l r e p r e s e n t e d b y G E O M A P . T h e s e t w oE G I ' s a r e a t f i r st m a t c h e d . T h e a t t i t u d e o f a n o b j e c t isa l s o a s s u m e d t o b e c o n s t r a i n e d i n a st a b le s ta t e . T h u s ,t h e p o s si b le c a n d i d a te s a r e f u r t h e r d e c r e a s e d .I f t h e o b j e c ts a r e a ll c o n v e x , a n E G I u n i q u e l yr e p r e s e n t s a n o b j e c t . O t h e r w i s e , i t c o r r e s p o n d s t om a n y o b j e c t s . F i g u r e 5 5 s h o w s a n e x a m p l e o fm u l t i p le c o r r e s p o n d e n c e s . T h e r e f o r e , f o r e a c h c a n -d i d a t e E G I , t h e c o r r e s p o n d i n g m o d e l s a r e e x a m i n e di n t h e n e e d l e m a p r e p r e s e n t a t i o n , a s s h o w n i n th ef i g u r e . P r a c ti c a ll y , a f e w m o d e l s m a y c o r r e s p o n d t o ac a n d i d a t e E G I . N e e d l e m a p s o f t h e s e c a n d id a t e s a r ea ls o s y n t h e si z e d f r o m m o d e l s i n G E O M A P , a n dm a t c h e d t o t h e o r i g i n a l n e e d l e m a p .E f f i c i e n t o b j e c t r e c o g n i t i o n is t h u s p e r f o r m e d b yt w o - st a g e m a t c h i n g , u s i n g t h e E G I r e p r e s e n t a t i o n a sa h i g h e r l e v el m o d e l r e p r e s e n t a t i o n . A n e x p e r i m e n th a s b e e n p e r f o r m e d f o r th e r e c o g n i ti o n o f p o l y h e d r aa n d c y l i n d e r . A f e w c a n d i d a t e s w e r e s e l e c t e d in t h ef ir s t s ta g e , a n d o n e c o r r e c t m o d e l w a s d e t e r m i n e d i nt h e s e c o n d s t a g e .

Re f e r e n c e s[ 1] B r o o k s , R . A . , 1 07 9 : T h e A C R O N Y M m o d e l - b a s e d v i s i o ns y s t e m , P r o c. 6 t h I n t e r n a t i o n a l J o i n t C o n f . o n A r t i f ic i a lIn te l l i genc e , pp . 105-113 .

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[2] C la rke , D . e t a l. , 1971 : Po la r ized l igh t and op t ica l m e asure -m e n t , P e r g a m o n P r e s s .[ 3] G u o , H . , Y a c h i d a , M . , a n d T s u j i , S. , 19 8 4: 3 - D m e a s u r e m e n to f t h e t i p s o f t h e l i n e - l i k e o b j e c t u s i n g s h a d o w i n f o r m a t i o n , J .Robot ics Soc ie ty o f Jap an , Vol . 2 , No . 2 , pp . 151-157 .[4 ] F ukada , Y . e t a l . , 1983 : Recog ni t ion o f s t ruc tu ra l ind us t r ia lp a r t s u t i l i z i n g r e l a t i o n s b e t w e e n e l e m e n t a r y b l o b s , P r o c .I n t e r n a t i o n a l C o n f . o n A d v a n c e d R o b o t i c s , p p . 1 5 5 - 1 6 2 .[ 5] F u k u i , I . , 1 9 82 : P o s i t i o n i n g b y m e a s u r e m e n t o f v i s u a l a n g l e sa n d i ts a p p l i c a t i o n , T r a n s . I E C E o f J a p a n , V o l . J 6 5 - D , N O . 4 ,pp . 428-434 .[ 6] F u k u s h i m a , T . e t a l ., 1 9 8 3: D e v e l o p m e n t o f a im a g ep r o c e s s o r L S I - I S P , P r o c . S p r i n g C o n f . o f I n f o r m a t i o nP r o c e s s i n g S o c i e t y o f J a p a n , p p . 9 3 9 - 9 4 0 .[ 7] H i r o s e , T . , 1 9 8 3: A r e c o g n i t i o n m e t h o d o f o b s ta c l e s u s i n g as t e r e o T V c a m e r a a s s e m b l y - A n a r t i f i c i a l e y e o f i n t e l l i g e n tv e h i c l e - , T r a n s . S I C E , V o l . 1 9 , N o . 9 , p p . 6 5 0 - 6 5 6 .[ 8] H o r n , B . K . P ., 1 9 7 5: O b t a i n i n g s h a p e s f r o m s h a d i n g i n f o r -m a t i o n , T h e P s y c h ol o g y o f C o m p u t e r V i s io n , W i n st o n , P . H .( e d . ) , M c G r a w - H i l l B o o k C o .[ 9] I k e u c h i , K . , 1 9 8 1 ( a) : D e t e r m i n i n g s u r f a c e o r i e n t a t i o n s o fs p e c u l a r s u r f a c e s b y u s i n g t h e P h o t o m e t r i c s t e r e o , I E E ET r a n s . , V o l . P A M I - 2 , N o . 6 , p p . 6 6 1 - 6 6 9 .[10] Ikeuc h i , K . , 1981 (b ) : Rec ogn i t ion o f 3 -D ob jec ts us ing thee x t e n d e d g a u s s i a n i m a g e , P r o c . 7 t h I n t e r n a t i o n a l J o i n t C o n f .o n A r t i f i ci a l I n t e l l ig e n c e , p p . 5 9 5 - 6 0 0 ,[ 11 ] I k e u c h i , K ., 1 9 8 3: D e t e r m i n i n g t h e v i e w e r d i r e c t i o n f r o m an e e d l e m a p b y u s i n g E x t e n d e d G a u s s i a n I m a g e , T r a n s .I E C E o f J a p a n , V o l . J 6 6 - D , N o . 5 , p p . 4 6 3 - 4 7 0 ,[12] Ikeuc h i , K . e t a l . 1982: A M odel based v is ion sys tem fo rr e c o g n i t i o n o f m a c h i n e p a r t s , P r o c. 2 n d A n n u a l N a t i o n a lConf . on Ar t i f ic ia l In te l l igence , pp . 18-21 .[ 13 ] h o . H . e t a l ., 1 9 84 : D i s t a n c e m e a s u r i n g m e t h o d u s i n g o n l ys i m p l e v is i o n c o n s t r u c t e d f o r m o v i n g r o b o ts , T r a n s . I E C E o fJap an , vo l . J67- D , No . 3 , pp . 265-272 .[14] K ida , Y . e t a l , 1983 : An im age p rocessor fo r in te l l igen tr o b o t s , P r o c . 1 st C o n f . o f R o b o t i c s S o c i e t y o f J a p a n , p p . 7 3 -74 .[ 15 ] K i m u r a , F . e t a l , 1 9 78 : P r o g r a m P a c k a g e G E O M A P , P r o c .G e o m e t r i c M o d e l P r o j e c t M e e t in g , C A M - I , I n c .[ 16] K i tagawa, H . e t a l . , 1983 : Ex trac t ion o f su r face g ra d ien t f roms h a d i n g i m a g e s , T r a n s . I E C E o f J a p a n , V o l . J 6 6 , N o . 1 , p p .65-72 .[17] K i tahash i , T . e t a l . , 1983 : 3 -D m o t ion ana lys is by m o noc u la ri m a g e s, I E C E o f J a p a n T R - P R L 8 3 - 5 0 , p p . 4 1 - 48 .[18] Kosh ikawa, K . e t a l . , 1983 : A m o del-b ased re cogn i t ion o fg l o ss y o b j e c ts , C o m p u t e r V i s i o n 2 5 - 3 , I n f o r m a t i o n P r o c e s s-i n g S o c i e t y o f J a p a n .

[ 19 ] K o s h i k a w a , K ., 1 9 8 2: A m e t h o d o f f i n d i n g s u r f a c e o r i e n t a -t i o n b y p o l a r i z a t i o n a n a l ys i s o f r e f l e c t e d l i g h t, T r a n s . S I C E ,Vol. 18, No. 10, pp. 77-79.[ 20 ] K u n o , Y . e t a l ., 1 9 8 3: R o b o t v i s io n i m p l e m e n t a t i o n b y h i g h -s p e e d i m a g e p r o c e s s o r T O S P I X , P r o c . I n t e r n a t i o n a l C o n f .on Advanced Robot ics , pp . 163-170 .[ 21 ] K u r o k a w a , H . e t a l ., 1 98 3 : T h e a r c h i t e c t u r e a n d p e r f o r m -a n c e o f I m a g e P i p e l i n e P r o c e s s o r , P r o c . V L S I ' 8 3 I n t e r n a t i o -na l Conf . , pp . 275-284 .[22] Masuda , R . , 1983 : An op t ica l p rox im i ty sensor and thea p p l i c a t i o n t o r o b o t c o n t r o l , P r o c . l s t e C o n f . o f R o b o t i c sS o c i e t y o f J a p a n , p p . 8 5 - 8 6.[ 23 ] M a t s u d a , F . e t a l. , 1 9 83 : R i n g o p e r a t o r f o r l a b e l i n g r a n g ep i c t u r e , T r a n s . I E C E o f J a p a n , V o l . J 6 6 - D , N o . 1 0 , p p . 1 1 61 -1168.

[ 24 ] M i n o u , M . , K a n a d e , T . a n d S a k a i , T . , 1 9 8 1 : A m e t h o d o ft i m e - c o d e d p a r a l le l p l a n e s o f li g h t f o r d e p t h m e a s u r e m e n t ,T r a n s . I E C E o f J a p a n , v o l . E 6 4 , N o . 8 , p p . 5 2 1 - 5 2 8 .[25] Matsuhs i ta , T . e t al ., 1983 : An a t tem pt to descr ibe func t ion so f t h e h a n d - e y e s y s t e m w i t h a r o b o t v i s i o n l a n g u a g e : R V L / A ,P r o c . I n t e r n a t i o n a l C o n f . o n A d v a n c e d R o b o t i c s , p p . 3 2 7 -334 .[26] Miyagaw a, M. , Ohk i , K . , and Kum agai , N . , 1983 : F lex ib lev i s io n s y s te m " M u l t i - W i n d o w " a n d t h i s a p p l ic a t i o n , P r o c .I n t e r n a t i o n a l C o n f . o n A d v a n c e d R o b o t i cs , p p . 1 7 1- 17 8 . .[27] Mor i , K . e t a l ., 1978 : D es ign o f loca l para l le l pa t te rnprocessor , P roc . AFIPS Conf . , Vol . 47 , pp . 1025-1031 .[ 28 ] N a k a m u r a , T . a n d U e d a , M . , 19 8 2: M a t c h i n g m e t h o d o fv e r t i c a l e d g e p a t t e r n s f o r l o c a t i n g a m o b i l e r o b o t , T r a n s .S ICE, Vol . 18 , No . 6 , pp . 576-582 .[ 29 ] N a k a m u r a , Y . a n d H a n a f u s a , H . , 1 9 8 3: A n e w o p ti c a lp r o x i m i t y se n s o r f o r t h r e e d i m e n s i o n a l a u t o n o m o u s t r a j e c -t o r y c o n t r o l o f r o b o t m a n i p u l a t o r s , P r o c . I n t e r n a t i o n a l C o n f .

o n A d v a n c e d R o b o t i c s , p p . 1 7 9 -1 8 6.[30] Naka tan i , H . e t a l , 1980 : Ex trac t io n o f van ish in g po in t and i t sa p p l i c a t i o n t o s c e n e a na l y s is b a s e d o n i m a g e s e q u e n c e , P r o c .5 t h I C P R , p p . 3 7 0 - 3 7 2 .[31] N ish ino , E . and Sh i ra i , Y . , 1984 : m eta l su r face shape f romp h o t o m e t r i c s t e r e o m e t h o d w i t h li g h t p ro j e c t io n , C o m p u t e rV i s i o n 3 1- 2 , I n f o r m a t i o n P r o c e s s i n g S o c i e t y o f J a p a n .[32] Ohta , Y . e t a l . , 1981: Ob ta in i ng sur fac e o r ien ta t ion f r omt e x e l s u n d e r p e r s p e c t i v e p r o j e c t i o n , P r oc . 7 t h I n t e r n a t i o n a lJ o i n t C o n f . o n A r t i f ic i a l I n t e l l i g e n c e , p p . 7 4 6 - 7 5 1 .[ 33 ] O s h i m a , M . a n d S h i r a i , Y . , 19 7 9: A s c e n e d e s c r i p t i o n m e t h o du s i n g t h r e e - d i m e n s i o n a l i n f o r m a t i o n , P a t t e r n R e c o g n i ti o n ,Vol. 11, No. 1, pp. 9-17.

[ 34 ] S h i r a i , Y . a n d S u w a , M . , 1 9 7 1: R e c o g n i t i o n o f P o l y h e d r o n sw i t h a R a n g e F i n d e r , P r o c . 2 n d I n t e r n a t i o n a l J o i n t C o n f . o nA r t i f i c i a l I n t e l l ig e n c e , p p . 8 0 - 8 7 .[ 35 ] S h i r a i , Y . 1 97 9 : T h r e e - d i m e n s i o n a l c o m p u t e r v i si o n , C o m -p u t e r V i s i o n a n d S e n s o r - B a s e d R o b o t s , G . G . D o d d a n d L .Rosso l (eds. ), P len um Press , pp . 187-205.[36] Sh i ra i , Y . e t a l . , 1983 : A v is ion sys tem based on th ree-d i m e n s i o n a l m o d e l , P r o c . I n t e r n a t i o n a l C o n f . o n A d v a n c e dRobot ics , pp . 139-146 .

[ 37 ] S u g i h a r a , K . , 1 9 79 : R a n g e - d a t a a n a l y s i s g u i d e d b y a j u n c t i o nd ic t ionary , Ar t i f ic ia l In te l l igence , Vol . 12 , No . 1 , pp . 41-69 .[38] Sug iya m a, K . e t a l . , 1983 : A v is ion sys tem (T he f i r s t repor t ) ,P r o c . l s t e C o n f . o f R o b o t i c s S o c i e t y o f J a p a n , p p . 6 9 - 7 0 .[ 39 ] T e m m a , T . e t a l ., 19 8 4: T e m p l a t e - c o n t r o l l e d i m a g e p r o c e s -s o r T I P S - 1, N E C R e s e a r c h a n d D e v e l o p m e n t , N o . 7 2 , p p . 3 3 -41 .[ 40 ] T e r a s h i , Y . e t a l. , 1 9 83 : R e c o g n i t i o n o f s u r f a c e g e o m e t r y o fg l o s sy o b j e c ts , P r o c . 1 4 t h J o i n t C o n f . o n I m a g e T e c h n o l o g y ,pp . 245-246 .[41] Tsu gaw a e t a l ., 1979: A n au to m o bi le w i th a r t i f ic ia l in te l l i -g e n c e , P r o c . 6 t h I n t . J o i n t C o n f . o n A r t i f i c i a l I n t e l l i g e n c e ,pp . 893-895 .[ 42 ] W o o d h a m , R . J ., 1 9 78 : P h o t o m e t r i c s t e r e o : a r e f l e c t a n c e m a pt e c h n i q u e f o r d e t e r m i n i n g s u r f a c e o r i e n t a t i o n f r o m i m a g ein tens i ty , P roc . SPIE , San D iego , Vol 155 .

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