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ti NCKH sinh vin 1 Nhn dng i tng

THNG TIN CHUNG V TI

1. Tn ti: Nhn dng tng th i tng

2. Cp d thi: Cp B

3. Nhm sinh vin thc hin:

STT H tn sinh vin Lp, khoa Vai tr

1 L nh Trng H Tin K10B, Khoa CNTT-TT Nhm trng

2 L Vn Ho H Tin K10B, Khoa CNTT-TT Nhm vin

3 L Ngc Thanh H Tin K10B, Khoa CNTT-TT Nhm vin

4. Gio vin hng dn:

- ThS. Trng Th Chuyn, Trng b mn khoa hc my tnh, khoa CNTT-TT.

- ng hng dn: ThS. Phm Th Anh, ThS. L nh Danh, khoa CNTT-TT.

5. Thi gian thc hin: 6 thng (t thng 11/2010 n thng 5/2011).

6. C quan qun l ti: Trng i hc Hng c.

7. n v ch tr ti: Khoa cng ngh thng tin v truyn thng

BNG CC K HIU, CH VIT TT DNG TRONG BO CO

K hiu, ch vit tt c hiu lCSDL C s d liuSIFT Scale-invariant feature transform

DoG Diffirence of GaussianNC Nghin cu

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MC LC

Mc Tn chng, mc v tiu mc TrangThng tin chung v ti 01Mc lc 02M u 03

I S cn thit ca ti 04

II Mc tiu ca ti 04III ngha khoa hc, tnh thc tin v kh nng ng dng 04IV Ni dung nghin cu 041 Tng quan v SIFT 052 Ni dung Gii thut 07

2.1 D tm cc tr cc b 072.2 Trch xut keypoint 102.3 Gn hng cho cc keypoint 122.4 To b m t cc b 133 K thut i snh 144 Nhng xut ci tin, pht trin gii thut SIFT 14V Kt qu 15VI Kt lun 18VII Ti liu tham kho 18

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M U

X l nh s (DIP - Digital Image Processing) ni chung v nhn dng i

tng (Object Recognition) ni ring, v ang c ng dng rt nhiu. N tri

rng trn mi mt trong cuc sng t x l cc bn in n, bo, ba tp ch n vn

v nng lc nhn trong my hc, i khi n rt gn gi trong cuc sng hng

ngy m nhiu ngi khng nhn ra nh chc nng nhn dng khun mt, i

tng chuyn ng trong cc my nh, my quay phim hay n gin l chc nng

camera ca in thoi i ng cng c tch hp cc cng c x l nh v nhn

dng i tng. cp su hn vo vn nhn dng i tng l cc bi ton c

th nh nhn dng du vn tay, nhn dng khun mt, nhn dng ch vit. Cc bi

ton ny c ng dng rt nhiu trong nhn dng i tng hnh s, phc v iu

tra, kim chng.

T nhng ng dng rng ri nh vy m ti cc t chc, cc trung tm

nghin cu ca cc trng i hc trn th gii, cc ti v x l nh lun c

u t nhiu v vt cht ln thi gian. Tuy trong nc, cc nm gn y mc d

cc nghin cu v x l nh v nhn dng i tng pht trin mnh nhng thng

qua cc cuc hi tho quc gia th vn ny cha nhn c nhiu kt qu nh

mong i v cn u t nhiu hn na.

Trong vn c th nhn dng i tng th ngy nay hng nghin cu ph

bin trn th gii l vic s dng cc im bt bin (Invarian Feature) trong nh lm

c trng (Keypoint) nhn dng. Tiu biu nht trong cc thut ton i snh s

dng keypoint dng ny l thut ton SIFT (Scale-Invarian Feature Transform,

David Lowe 1999 v 2004), SIFT c th coi l thut ton tin cho cc ng dng

cng nh gii thut khc v bin i c trng bt bin trong nh. Cc gii thut

ang ng dng trong thc t khc u da trn hay pht trin theo cc nhnh ring

ca SIFT.

Cc c trng trong SIFT khng ph thuc vo cc php bin i nh c bn

nh xoay, thu phng, thay i sng... nn c th xem tp cc c trng ca mtnh l th hin cho ni dung ca nh . V vy kt qu ca vic nhn dng s c

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chnh xc rt cao v thm ch c th khi phc c i tng b che khut trong

nh. Tuy nhin gii thut SIFT rt phc tp trong ci t, i hi thi gian nghin

cu v am hiu nhiu thut ton thnh phn.

Trong phm vi ti, em tm hiu su tng bc ca gii thut SIFT vthc hin ci t thnh cng gii thut v ng dng vo nhn dng i tng ty

bin trong c s d liu c trng c trch chn t nh.

I. S cn thit ca ti

Mc d c ng dng nhiu trn th gii nhng trong nc vic tm hiu v

ci t cc thut ton nhn dng s dng keypoint ang cn hn ch, hin ti rt

him cc ti, cng trnh nghin cu, ti liu ting Vit v vn ny. V vy victm hiu, hon thnh ci t c bn thut ton l cn thit.

II. Mc tiu nghin cu

Chng em thc hin ti vi mc tiu chnh l tm hiu, nm r thut ton

SIFT v k thut d tm c trng bt bin. Sau , da theo nhng iu tm hiu

c, chng em s ci t tng phn ca thut ton v vit ti liu ting Vit cng

m ngun nu nhng hiu bit y v gii thut SIFT.

III. ngha khoa hc, tnh thc tin v kh nng ng dng

Hin nay trn th gii v ang c nhng bc tin nhanh trong lnh vc

tra cu nh da trn ni dung, tuy nhin vic nghin cu, ng dng cc thnh qu

ny trong nc cn rt khim tn. V ngha khoa hc th ti tm hiu v

nhng thut ton mnh nht hin nay v trch chn c trng v t nhng bc tmhiu xut gii php ci thin chi ph tnh ton nhng vn gi c chnh

xc cn thit.

T vic hiu v ci t nhng thut ton mnh trn, s thc hin xy dng

mt chng trnh hon chnh c th i snh tnh tng ng cc i tng

trong cc nh khc nhau hoc tm kim i tng trong CSDL ln cc nh.

IV. Ni dung nghin cu

Ni dung nghin cu ca ti bao gm:

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- Tm hiu cc thut ton nhn dng i tng s dng c trng bt bin,

trong trng tm l thut ton SIFT.

- Thc hin ci t thut ton SIFT v xy dng chng trnh nhn dng

i tng s dng SIFT.

Sau y chng em xin trnh by c th ni dung nghin cu ca ti.

1. Tng quan v SIFT

Mt thut ton tiu biu v c hiu qu kh cao l da theo cc c trng cc

b bt bin trong nh: SIFT (Scale-invariant Feature Transform) do David Lowe

a ra t nm 2004 v n nay c nhiu ci tin trong thut ton. c trng c

trch chn trong SIFT l cc im c bit (keypoint), cc im ny km theo cc

m t v n v mt vc t c ly keypoint lm im gc.

C bn giai on chnh c thc hin trong thut ton trch xut cc

im c bit v cc c trng ca n bao gm:

a. D tm cc tr trong khng gian o (Scale-space Extrema Detection): Bc

u tin ny s p dng hm sai khc Gaussian (DoG - Deffirence of

Gaussisan) tm ra cc im c kh nng lm im c trng tim nng(candidate keypoints), l nhng m rt t ph thuc (bt bin) vo s thu

phng nh v xoay nh.

b. Lc v trch xut cc im c bit (Keypoint localization): T nhng im

tim nng trn s lc v ly ra tp cc im c trng tt nht (keypoints).

c. Gn hng cho cc im c trng (Oriented Assignment): Mi im c

trng s c gn cho mt hoc nhiu hng da trn hng gradient canh. Mi php ton x l cc bc sau ny s c thc hin trn nhng

d liu nh m c bin i tng i so vi hng gn, kch c v

v tr ca mi im c trng. Nh , to ra mt s bt bin trong cc php

x l ny.

d. B m t im c trng (Keypoint Description): Cc hng gradient cc b

c o trong nh c kch c c th no trong vng ln cn vi mi im

c trng. Sau , chng s c biu din thnh mt dng m cho php m

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t cc tng quan trng ca qu trnh bp mo hnh dng cc b v s thay i

v sng.

Tp cc im c bit thu c thng ph thuc rt t vo cc php bin i

c bn nh xoay, phng to, thu nh, tng gim cng sng, v vy c th xem

y l cc c trng mang tnh cc b ca nh. i snh v nhn dng hai nh

th ta tm tp keypoint ging nhau trong hai nh, da vo hng v t l c th

bit i tng trong nh gc xoay, thu phng bao nhiu so vi nh em i snh.

Cch tip cn ca thut ton ny da vo im bt bin cc b ca nh, chng c

trch xut ra, c nh hng v m t sao cho hai keypoint hai vng khc nhau

th khc nhau. Tuy nhin mt yu t nh hng khng nh n tc thut ton l

s lng cc keypoint c ly ra l khng nh. Trung bnh mt nh kch thc 500x 500 pixels th s trch xut c khong 1000 im (s lng im ny ph thuc

vo ty tng nh v tham s lc khc nhau). S lng cc im c trng c mt

tm quan trng trong vn nhn dng i tng, nhn dng mt i tng nh

trong mt nh cha tp hp cc i tng hn n th cn t nht 3 im c trng

ging nhau pht hin v v bc tch i tng.

i vi vn xy dng mt c s d liu nh v thc hin nhn dng i

tng bt k th ban u thng s dng SIFT to mt h d liu cc c trng

(keypoints) c trch xut t d liu nh gc. Sau vi mi nh i tng em

nhn dng ta dng gii thut SIFT trch xut tp c trng t nh v em i snh

vi h d liu c trng tm ra tp keypoint ging nhau, t nhn dng i

tng trong c s d liu nh ban u. Tuy nhin vic i snh ny cn chi ph i

snh rt ln i vi c s d liu nh c s lng ln do s lng cc c trng

mi nh l ln.

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Hnh 1: Minh ha cc bc chnh trong gii thut SIFT

2. Ni dung gii thut

2.1. D tm cc tr cc b

Nh nu trn, bc u tin s tm cc im tim nng c th tr thnh

im c trng bng phng php lc theo tng da vo vic thay i tham s b

lc Gaussisan. Trong bc ny, ta cn d tm cc v tr v cc s o (kch c) m

chng bt bin trong cc khung nhn khc nhau ca cng mt i tng. Cc v tr

bt bin v s o c th c d tm bng cch tm kim cc c trng n nh

trn ton b cc s o c th, s dng mt hm lin tc v s o vn rt ni ting c

tn l hm khng gian o (Witkin 1983).

Theo cc cng b ca Koenderink (1984) v Lindeberg(1994) th hmGaussian l hm tt nht biu din khng gian o ca nh 2 chiu. V vy, khng

gian o ca mt nh s c nh ngha nh l mt lm L(x,y,) c to ra bng

cch nhn chp nh gc I(x,y) vi mt hm Gaussian G(x,y,) c tham s v s o

thay i.

L(x,y,) = G(x,y, ) * I(x,y) (2.1.1)

Trong ton hng * l php nhn chp cc ma trn 2 chiu x,y. V G(x,y, )hm Gaussian:

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G(x,y,) = (2.1.2)

tm nhng im c trng c tnh bt bin cao, thut ton c s dng l

tm cc tr cc b ca hm sai khc DoG (Difference-of-Gaussian), k hiu lD(x,y, ). Hm ny c tnh ton t s sai khc gia 2 khng gian o cnh nhau

ca mt nh vi tham s o lch nhau mt hng s k.

D(x,y,) = L(x,y,k) L(x,y,) = (G(x,y,k) G(x,y, )) * I(x,y)

(2.1.3)

Cc l do la chn hm Gaussian l v n l k thut rt hiu qu tnh ton

L (cng nh lm tng mn ca nh), m L th lun phi c tnh rt nhiu m t c trng trong khng gian o, v sau , D s c tnh mt cch n gin

ch vi php tr ma trn im nh vi chi ph thc hin thp.

Hnh 2. Qu trnh tnh khng gian o (L) v hm sai khc D

Hn na, hm sai khc DoG c th c s dng to ra mt s xp x gn

vi o hm bc hai Laplace c kch thc chun ca hm Gaussian (2

2

G) do tcgi Lindeberg xut nm 1994. ng ch ra rng vic chun ha o hm bc

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hai vi h s 2 l cn thit cho bt bin o tr nn ng. C th, ng cng b

rng cc gi tr cc i v cc tiu ca 22G chnh l nhng gi tr c tnh n nh

nht (bt bin cao) so vi mt lot cc hm nh gi khc nh : gradient, Hessian

hay Harris.

Mi quan h gia D v 22G c biu din nh sau:

(2.1.4)

Nh vy, 2G c th c tnh thng qua vic xp x s sai khc hu hn

ti cc tham s o gn nhau k v :

(2.1.5)

Do : (2.1.6)

T cng thc ny, ta thy khi m hm sai khc DoG c tnh ton ti cc

tham s o lch nhau mt hng s k, th ta c th s dng DoG xp x o hm

bc hai Laplace ca Gaussian. V h s (k-1) trong phng trnh trn l hng s

trong mi khng gian o nn n s khng nh hng n vic tm cc v tr cc tr.

Sai s trong vic xp x o hm bc 2 tin v 0 khi k gn vi 1. Tuy nhin, cc kt

qu th nghim ca tc gi cho thy qu trnh xp x o hm khng nh hng n

vic d tm cc v tr cc tr thm ch ngay c khi chn k kh xa, v d k= 2 .

Sau khi p dng hm DoG ta thu c cc lp kt qu khc nhau (scale) t

nh gc, bc tip theo l tm cc cc tr trong cc lp kt qu theo tng min cc

b. C th l ti mi im trn cc lp kt qu s c so snh vi 8 im ln cntrn cng lp v 9 im ln cn trn milp khc (hnh di).

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Hnh 3. Qu trnh tm im cc tr trong cc hm sai khc DoG

Trong hnh trn: im nh du x s c so snh vi 26 im ln cn (nhdu vng trn xanh). im ny s c ly lm im tim nng (im c th lm

im c bit candidate keypoint) nu n c gi tr ln nht hoc nh nht so vi

26 im ln cn nh trn. Gii php cho vic tm cc im tim nng ny l s dng

thut ton blob detection (d tm im) do Lindeberg xut.

V s lng cc cc tr l rt ln, v vy tng s hiu qu khi d tm cc

im cc tr (d cc im cc tr tt nht thay v phi d ht), ta cn xc nh tn s

ly mu trong khng gian o v tn s ly mu trong khng gian quan st (khng

gian nh). Tht khng may l ta khng th xc nh c 2 loi tn s ny mt cch

ng trong mi tin trnh d tm. Thay v vy, cc tn s ny s c xc nh

offline thng qua phng php th nghim. Sau khi th nghim vi nhiu ngun d

liu nh khc nhau, tc gi ch ra tn s ly mu trong khng gian o tt nht l 3

(gi li 3 lp trong mi b 8 lp), v tn s ly mu = 1.6.

2.2. Trch xut keypoint

Sau bc 1 s thu c rt nhiu im tim nng c th lm im c bit,

tuy nhin mt s trong chng l khng cn thit. bc tip theo ny s loi b cc

im c tng phn km (nhy cm vi nhiu) hoc tnh c trng cc b t hn

cc im khc hoc c xu hng l ng bin i tng. Bc thc hin ny gm

3 cng on:

a. Php ni suy ln cn cho v tr ng ca im tim nng:

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Php ni suy ln cn () s dng m rng Taylor (Taylor expansion) cho hm

Difference-of-Gaussian D(x,y,):

(2.2.1)

Trong : D v o hm ca n c tnh ti mt im tim nng v X =

(x,y,) l khong cch t im . V tr ca im cc tr c xc nh bng

cch ly o hm ca hm trn vi i s X v tin dn n 0 :

Hnh 4 : M phng s dng cng thc m rng ca Taylor cho hm DoG

(2.2.2)

Nu > 0.5 theo mt chiu no th n c ch s cc tr khng gn vi cc

im tim nng khc, n s b thay i v php ni suy s thay th vai tr ca n

bng im khc gn n.

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Hnh 5. Minh ha cc bc ca qu trnh la chn cc im keypoints. (a) l

nh gc. (b) m t 832 im keypoints tm c, cc im keypoints c v

dng mt vector th hin 3 thng tin : v tr, hng v di. (c) sau khi

t ngng tng phn ti thiu, ta gi li c 729 im. (d) Gi li 536

im sau khi p mt ngng na v h s cong.

b. Loi tr cc im c tnh tng phn km:

Cc im nhy cm vi sng v nhiu th khng c tr thnh im c

bit v cn loi b khi danh sch im tim nng. Trong khai trin Taylor m rng

trn, nu im tim nng no c gi tr < 0.03 th im s b loi, ngc li

th n c gi li theo v tr mi (y+ ) v ty bin , vi y l v tr c ca n cng

gi tr bin .

c. Loi b cc im d tha theo bin :

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S dng hm DoG s cho tc ng mnh n bin khi v tr ca bin l kh

xc nh v v vy cc im tim nng trn bin s khng bt bin v b nhiu. V

tng s n nh cho cc im s c chn lm im c bit ta s loi tr cc

im tim nng kh nh v (tc l v tr d thay i khi c nhiu do nm bin).

Sau khi p dng hm DoG s lm ng bin nh khng r rng v cong

chnh s c gi tr ln hn nhiu so vi cong dc theo bin v vy cn loi b bt

cc im c bit dc theo cng mt bin. Gii php cho vic ny l s dng gi tr

ca ma trn Hessian cp 2:

(2.2.3)

Cc gi tr ring ca H t l thun vi cong ca D, cc gi tr ring (gi

tr nh) v (gi tr ln) c t l r = / s c s dng. Cc phn t ca H l Dxx

v Dyy

2.3. Gn hng cho cc keypoint

Bng vic gn mt hng cho mi im c trng keypoint da vo cc

thuc tnh nh cc b, b m t keypoint c th c biu din tng i so vi

hng ny v do t c tnh bt bin i vi cc hin tng quay nh. Cch

tip cn ny ngc li vi cc b m t bt bin hng ca Schmid (1997) ch

mi thuc tnh nh s da vo mt o bt bin v hng. Nhc im ca cch

tip cn ny l n gii hn s lng cc b m t c s dng v b qua cc

thng tin v nh bi v n khng yu cu mi o u phi da trn mt hng

nht qun.

Sau y l k thut gn hng cc b cho cc im c trng. o cacc im c trng c s dng tm ra mt nh lc Gaussian L vi kch

thc gn nht sao cho mi tnh ton s c thc hin trong cng mt cch bt

bin v o. Vi mi mu nh L(x,y) ny, gi m(x,y) l bin gradient, (x,y)

l hng. Hai gi tr cui c tnh ton nh sau:

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(2.3.1)

2.4. To b m t cc b

Cc php x l trn y thc hin d tm v gn ta , kch thc, v

hng cho mi im c trng keypoint. Cc tham s yu cu mt h thng ta a phng 2D c th lp li c m t vng nh a phng v nh vy to

ra s bt bin i vi cc tham s . Bc tip theo y s tnh ton mt b m t

cho mt vng nh a phng m c tnh c trng cao (bt bin vi cc thay i

khc nhau v sng, thu phng nh, xoay).

Mt cch tip cn n gin l ly mu mt nh cc b ln cn

im c trng mt o thch hp, v i snh cc mt ny s dng o

tng quan chun. Tuy nhin, h s tng quan n gin th li rt nhy cm vi s

thay i m gy ra s ng k nhm cc mu, chng hn nh cc bin i Affine,

phi cnh 3D, hoc bp mo mm. Cch tip cn tt hn nhiu c a ra bi

Edelman, Intrator v Poggio (1997). Cch tip cn ny da trn mt m hnh th

gic sinh hc, c th l m hnh noron phc tp trong h thng no b. Cc noron s

tng ng vi mt gradient ti mt hng v tn s khng gian c th, nhng v tr

ca gradient trn vng mc c php trt trn mt phm vi nh ca khung nhn.Da trn cch tip cn ny, tc gi ci t b m t mi trong cho php vic

trt v tr s dng mt cch tnh ton khc.

Hnh sau m phng qu trnh tnh ton cc b m t theo cch tip cn mi.

Hnh 6: M t to b m t cc b

nh tri l m phng bin gradient v hng ti mi mu nh trong mt

vng ln cn vi im keypoint. Cc gi tr tp trung trong mt ca s gaussian

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(nm bn trong vng trn). Cc mu ny sau c gom li thnh mt lc

hng m t vn tt ni dung trong 4x4 vng con nh c m t bn phi vi

di ca mi hng tng ng vi tng bin gradient gn hng bn trong mt

vng.

3. K thut i snh

Trc ht i snh cc nh vi nhau th cn trch xut tp keypoint tng

ng t mi nh bng cc bc ch ra trn. Sau vic i snh s thc hin

trn cc tp keypoint ny. Bc chnh trong k thut i snh s thc hin tm tp

con keypoint so khp nhau hai nh, thc hin vic ny s tm cc cp keypoint

trng nhau ln lt hai nh. Tp con cc keypoint so khp chnh l vng nh

tng ng.

Cc bc i snh trn l gn ging vt cn, i vi vic i snh trong

CSDL ln cc nh s gp rt nhiu kh khn do chi ph thc hin i snh l rt

ln. Gii php cho vn ny l to ch mc (index) cho cc tp keypoint, c th l

cc keypoint c sp xp sn theo mt tiu ch da trn cc gi tr ca b m t v

hng ca n. thm mt tp keypoint mi s t ng sp xp v to ch mc cho

n. Vic thc hin tm kim mt keypoint so khp trn CSDL s p dng tm kimnh phn trn cc tp keypoint c sp xp v nh ch mc. Vic tm kim tp

keypoint so khp s tr nn n gin hn vi chi ph tnh ton thp hn nhiu.

4. Nhng xut ci tin, pht trin thut ton SIFT

a. Gim s lng keypoint trch xut mi nh

Mt trong nhng hn ch ln ca SIFT l chi ph i snh tp keypoint

kh ln do s lng keypoint trch xut c kh ln. khc phc nhc imny ngoi ci thin thut ton i snh th nn gim s lng cc im keypoint

trch xut c.

C th s ti gin tp keypoint theo cc bc:

- Hn ch cc im keypoint m ti bin Gradient bin i khng

r rt, tc l ti bin thin mu sc l nh, cc im ln cn c

mc xm gn tng t nhau. Hn ch nhng im ny khng lm gimtnh c trng ca tp keypoint do mt trong s cc im ln cn ca

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n c bin Gradient bin i r rt hn v c chn lm keypoint

s i din tnh c trng cc b cho im b loi b v im c

chn s ng thi tng tnh c trng cc b

- Kt hp vi vic d bin nh hn ch cc im keypoint nm gn

bin do cng gn bin s cng d b nh hng bi nhiu v cc php

bin i thng thng. Vic d bin s p dng k thut d bin Candy

cho ng bin mnh v hiu qu.

b. Dng lt mu loi tr trc cc nh t kh nng tng ng:

Trc khi thc hin tm kim trong CSDL nh ta s dng lt mu

loi cc nh c mu sc qu khc nhau, tc l tnh tng t nhau l rt t. Cc nh

ny s b b qua trong qu trnh tm kim gim chi ph i snh, tm kim nh

tng ng

c. Trch xut i tng ra khi nh theo i tng mu

Sau khi nhn dng i tng trong nh theo mt i tng mu ta c th

trch xut i tng ra khi nh nh vic iu chnh hng cho tng cp

keypoint so khp cho chng ng b nhau v thc hin d vt theo cc cht l cc

keypoint so khp ln theo bin i tng v thc hin trch xut c i tng

ra khi nh.

V. Kt qu

Chng em ci t thut ton SIFT v xy dng chng trnh nhn dng

nh tng ng trong mt CSDL nh c thu thp v d tm c trng trc .

Do y ch l th nghim nn ch tm trn s lng nh nh gm 50 nh kch thc

t 100x100 n 1024x1024.

tm t chc lu tr keypoint v tm kim keypoint tng ng, chng

trnh c thit k ring b hc s dng cu trc lu tr KDTree (cy a chiu) v

tm keypoint tng ng bng thut ton Nearest-Neighbor (tm hm xm gn

nht).

Sau y l minh ha kt qu ca chng trnh:

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Hnh 7: nh bn phi (nh b) c trong CSDL v nh bn tri (nh a) l

nh u vo. Chng trnh nhn ra chnh xc cp nh tng ng ny vi

chnh xc cao (49 keypoint so khp). iu ny cho thy SIFT bt bin vi php

xoay, thu phng v khng yu cu tnh ton vn ca nh.

Hnh 8: Ln ny chng trnh th nghim vi nh u vo l mt khun mt

i tng c th trong mt nh t CSDL c cha i tng. Chng trnh nhn

din chnh xc v khoanh vng i tng nhn din nh trong hnh.

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Hnh 9: y l th nghim nhn dng a im qua nh v tinh. Hnh bn

phi l nh u vo, nh chp t trn v tinh cnh khun vin c s 1 trng H

Hng c, nm 2003 v khu vc ln cn (bi Google Maps). nh u vo l chp

nh a nng ca trng v b xoay mt hng khc vi gc nhn thay i. Chng

trnh tm ra vi chnh xc rt cao, vi 135 keypoint so khp.

Hnh 10: y l th nghim chng trnh nhn dng mt i tng c th trong

nh, hnh bn tri l i tng mu, hnh phi l bi cnh cha i tng mu, ktqu chng trnh nhn ra i tng vi 69 keypoint so khp

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VI. Kt lun

SIFT c xem l thut ton rt mnh v thuc loi phc tp nht trong nhn

dng i tng. V vy, trong phm vi ti chng em ch tm hiu v ci t thut

ton vi y cc bc mc c bn, sau xy dng ng dng m phng vicnhn dng i tng tng ng s dng SIFT. ci tin v pht trin thnh mt

ng dng y cn phi u t thi gian v c phng php nghin cu mc

cao hn. Trong thi gian ti, chng em s tm hiu thm cc ci tin ca SIFT v

xy dng ng dng hon chnh bao gm c giao din ngi dng v CSDL c

trng mt cch y , h thng.

VII. Ti liu tham kho

1/ Lowe, D. Distinctive image features from scale-invariant keypoints

International Journal of Computer Vision, 60, 2 (2004).

2/ Lee, David. Object Recognition from Local Scale-Invariant Features (SIFT).

O319.Sift.ppt.

3/ Basri, R., andJacobs, D.W.1997. Recognition using region correspondences.

International Journal ofComputerVision, 25(2).

4/ Brown, M. And Lowe, D.G. 2002. Invariant features from interest point groups.

In British Machine Vision Conference, Cardiff, Wales.

5/ Vedrana Andersen, LarsPellarin, ReneAnderson. Scale-Invariant Feature

Transform (SIFT): Performance and Application. In The IT University of

Copenhagen.

6/ Piotr Indyk. Algorithms for Nearest Neighbor Search Slide. In MIT.

7/ RobHess. An Open-Source SIFT Library. In School of EECS, Oregon State

University Corvallis, Oregon, USA.

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