DATN_PhanMemPhatHienNguoiTeNga

Thit k Phn mm pht hin ngi t ng s dng Camera

Mc lc

Li ni u .............................................................................................................. 1

Tm tt n .......................................................................................................... 2

Danh sch hnh v ................................................................................................... 4

Danh sch cc bng biu.......................................................................................... 7

Phn m u ............................................................................................................ 8

Chng 1: Tng quan vn pht hin t ng nhng ngi cao tui. ................. 10

1.1. Thc trng vn ng ngi cao tui .................................................... 10

1.2. Vai tr ca h thng pht hin t ng ngi ln tui .............................. 11

1.3. Cc h thng Pht hin t ng c trn th gii ...................................... 12

1.3.1. Tng quan cc h thng hin c ......................................................... 12

1.3.2. Thit b pht hin t ng s dng cm bin ........................................ 15

1.3.3. Thit b pht hin t ng s dng camera ........................................... 22

1.3.4. So snh cc h thng hin c ............................................................. 30

1.4. Phng php thc hin ti ................................................................... 31

Chng 2: C s l thuyt ..................................................................................... 33

2.1. Gii thiu v h thng x l nh ............................................................... 33

2.2. Thu nhn nh ............................................................................................ 35

2.2.1. Cc thit b thu nhn nh .................................................................... 35

2.2.2. Ly mu v lng t ha ................................................................... 37

2.2.3. Mt s phng php biu din nh .................................................... 39

2.2.4. Cc nh dng nh c bn .................................................................. 40

2.3. X l nng cao cht lng nh ................................................................. 40

2.3.1. Ci thin nh s dng cc ton t im .............................................. 41

2.3.2. Ci thin nh dng ton t khng gian (Spatial Operators) ................ 44

2.3.3. Cc php ton hnh thi hc ............................................................... 47

2.3.4. Khi phc nh .................................................................................... 49

2.4. Phng php pht hin bin ..................................................................... 51

2.4.1. K thut pht hin bin ...................................................................... 51

2.4.2. Phng php pht hin bin cc b .................................................... 52


ii

2.4.3. Pht hin bin gin tip ...................................................................... 55

2.5. Phn vng nh .......................................................................................... 56

2.5.1. Phn vng nh da vo ly ngng.................................................... 56

2.5.2. Phn vng da vo ng bin .......................................................... 57

2.5.3. Phn vng da theo min/vng .......................................................... 58

2.6. Nhn dng nh v nn nh ........................................................................ 60

2.6.1. Nhn dng nh ................................................................................... 60

2.6.2. Nn nh ............................................................................................. 60

2.7. Cc k thut hu x l .............................................................................. 61

2.7.1. Rt gn s lng im biu din ........................................................ 61

2.7.2. Xp x a gic bi cc hnh c s ....................................................... 63

Chng 3: Xy dng h thng pht hin t ng ngi ln tui. .......................... 66

3.1. Cc cng c s dng ................................................................................ 66

3.1.1. Microsoft Visual C++ ........................................................................ 66

3.1.2. OpenCV trn nn Visual C++ ............................................................ 67

3.2. Xy dng h thng pht hin t ng .......................................................... 69

3.2.1. Thu nhn Video ................................................................................. 69

3.2.2. Tch i tng ra khi khung nn ...................................................... 70

3.2.3. Xc nh t l khung, gc ................................................................... 75

3.2.4. Xc nh ng da vo t l khung, gc ............................................... 75

3.2.5. Pht tn hiu cnh bo ........................................................................ 78

3.2.6. Lu thut ton ............................................................................... 79

3.3. Kt qu thu c. ..................................................................................... 80

3.3.1. Giao din ca h thng ...................................................................... 80

3.3.2. Tng th h thng Kt qu .............................................................. 83

Chng 4: Kt lun ............................................................................................... 87

4.1. Cc kt qu t c ............................................................................ 87

4.2. Nhng tn ti v hng pht trin ............................................................ 87

Ti liu tham kho ................................................................................................. 88


Li ni u

Chng em xin chn thnh cm n Thy gio, Thc s Nguyn Vit Dng, ngi

hng dn tn tnh ch bo chng em rt nhiu trong sut qu trnh tm hiu

nghin cu v hon thnh n ny t l thuyt n ng dng. S hng dn ca

thy gip chng em c thm kin thc v lp trnh v kin thc v x l nh.

ng thi chng em xin chn thnh cm n cc thy c trong Vin in t -

Vin thng trng i hc Bch Khoa H Ni, cng nh cc thy c trong trng

trang b cho chng em nhng kin thc c bn cn thit trong sut thi gian hc

tp ti trng chng em c th hon thnh tt n ny.

Trong qu trnh hc cng nh trong sut thi gian lm tt nghip khng trnh

khi nhng thiu st, chng em rt mong c s gp qu bu ca cc thy c

cng nh tt c cc bn kt qu ca em c hon thin hn.

Sau cng, chng em xin gi li cm n n gia nh bn b to mi iu

kin chng em xy dng thnh cng n ny.

Chng em xin chn thnh cm n!

H Ni, ngy 10 thng 06 nm 2013

Nhm Sinh vin

Thi Ton t Anh c Dng Hong Hi


2

Tm tt n

Trong khun kh ti tt nghip, cng vi vic tm hiu cc bi bo trong v

ngoi nc, tm hiu cc sn phm thng mi lin quan n pht hin t ng, kt

hp vi l thuyt x l nh, nhm em nghin cu v a ra phn mm pht hin t

ng s dng mt camera quan st. Ni dung ca n gm c:

Chng 1: Tng quan vn pht hin t ng nhng ngi ln tui

Chng ny a ra vn nguy c ng v vai tr ca h thng pht hin t ng

ngi ln tui. Nhm nghin cu s khi chc nng ca h thng pht hin t

ng ni chung v mt s sn phm thng mi lin quan. T a ra phng

php nghin cu, thc hin ti ca nhm.

Chng 2: C s l thuyt

Chng ny ni v c s l thuyt x l nh, bao gm cc k thut nng cao

cht lng nh, cc phng php pht hin bin, phn vng nh v cc k thut hu

x l nh. y u l nhng l thuyt x l nh quan trng m nhm nghin cu

p dng vo ti.

Chng 3: Xy dng h thng pht hin t ng ngi ln tui

Chng ny a ra cc cng c m nhm s dng nghin cu ti, bao

gm Microsoft Visual C++ v OpenCV. Da vo 2 cng c trn, nhm xy dng

nn h thng pht hin t ng ngi ln tui dng mt camera quan st. Chng 3

cn cp n cu trc h thng nhm lm v cc kt qu t c.

Chng 4: Kt lun

Sau 3 thng nghin cu nhm hon thnh ti vi kt qu cao. Phn mm

pht hin t s dng mt camera quan st ca nhm thc hin c vi chnh

xc ln ti 90%. Trong tng lai, nhm hy vng c th pht trin phn mm thnh

sn phm thng mi.


3

ABSTRACT

In the framework of the thesis, along with the understanding of the paper at

home and abroad, the refering of the trade products related to detecting falls

combined with theoretical image processing, we researched and provided a software

to detect falls using a single camera. The content of our project include:

Chapter 1: Overview of the problems of Fall Dectection at older people

This chapter find out some fall risks and a role of Fall detection systems at

senior citizen. We find out about som function block diagram and some commercial

products involved of the Fall detection system. Since then we provide the research

methods, to implement our project.

Chapter 2: Theoretical Foundations

This chapter is about the image processing theoretical basis, including the

techniques for improving the quality of digital image, edge detection methods,

image segmentation and image post-processing techniques. Those are the important

theories of image processing that we research to apply for our project.

Chapter 3: Building Fall detection system for elderly people

This chapter offers the tools that we used to research, including Microsoft

Visual C + + and OpenCV. Based on them, we built the Fall detection system for

elderly people using a single camera. Chapter 3 also refers to the system

architecture that we has made and the results we achieved.

Chapter 4: Conclusion

After 3 months of the studying, we completed our project with a good results.

The Fall detection software using a single camera have an accuracy up to 90%. In

the future work, we hope to develop this software into the commercial products.


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Danh sch hnh v

Hnh 1.1: S khi tng quan h thng kt hp cm bin v camera ............ 14

Hnh 1.2: M t h thng SmartCane .............................................................. 15

Hnh 1.3. Cu trc h thng, tng tc d liu gia cc nt cm bin ............ 19

v nt gim st ................................................................................................ 19

Hnh 1.4. Kt qu phn loi da trn m hnh SVM ........................................ 21

Hnh 1.5. Minh ha vector Vv v Vh............................................................... 24

Hnh 1.6. Tng quan v thut ton pht hin t ng ......................................... 25

Hnh 1.7. M t hnh elip bao quanh ngi ..................................................... 28

Hnh 2.1 Cc bc c bn trong x l nh ...................................................... 33

Hnh 2.2 S phn tch v x l nh, v lu thng tin gia cc khi ........ 35

Hnh 2.3 H ta RGB ................................................................................. 37

Hnh 2.4 Cc dng mu im nh. ................................................................... 38

Hnh 2.5 Hng cc im bin v m tng ng : A11070110764545432 ...... 39

Hnh 2.6 Dn tng phn ........................................................................... 41

Hnh 2.7. Tch nhiu v phn ngng ............................................................. 42

Hnh 2.8. Bin i m bn. .............................................................................. 42

Hnh 2.9. Thc hin gn ng cn bng mc xm ...................................... 43

Hnh 2.10 nh sau Dilation. ............................................................................ 48

Hnh 2.11 nh sau php erosion...................................................................... 48

Hnh 2.12 nh sau open v close. ................................................................... 49

Hnh 2.13 Qu trnh pht hin v lu tr nh .................................................. 50

Hnh 2.14 K thut lc ngc ......................................................................... 51

Hnh 2.15 Dng phn b (profile) sng v vi phn bc nht (gradien) ca

ng vin 1 chiu thng thng. ......................................................................... 52

Hnh 2.16 M hnh pht hin ng bin dng ton t Gradient ..................... 53

Hnh 2.17 Profile sng, vi phn bc nht v bc hai (Laplace) ca ng

vin 1 chiu thng thng ..................................................................................... 54

Hnh 2.18 M hnh pht hin ng bin dng ton t Laplace ...................... 55

Hnh 2.19 Phn vng nh. ............................................................................... 56


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Hnh 2.20 Cc bc phn vng da vo ng bin ....................................... 57

Hnh 2.21 Pht trin vng lin kt trng tm (CLRG) ..................................... 59

Hnh 2.22 S tng qut h thng nhn dng nh ......................................... 60

Hnh 2.23: n gin ha ng cng theo thut ton Douglas Peucker .......... 61

Hnh 2.24: n gin ha ng cong vi thut ton Band Width .................... 62

Hnh 2.25: n gin ha ng cong vi thut ton Angles ........................... 63

Hnh 2.26: Xp x a gic bng ng trn ..................................................... 64

Hnh 2.27: Xp x a gic bng hnh ch nht ................................................. 65

Hnh 3.1: S khi h thng ......................................................................... 69

Hnh 3.2: S khi tin x l ....................................................................... 70

Hnh 3.3: nh RGB v nh mc xm ca nn v khung hnh .......................... 71

Hnh 3.4: Lc trung v ..................................................................................... 71

Hnh 3.5: hnh nh th nghim thut ton tm s sai khc gia hai nh mc xm

.............................................................................................................................. 72

Hnh 3.6: Kt qu tm s sai khc gia nh nn v khung hnh trong video ..... 72

Hnh 3.7: nh sai khc sau khi lc trung v ..................................................... 73

Hnh 3.8: Nh phn ha vi cc mc ngng khc nhau .................................. 73

Hnh 3.9: Lp y l trng bng php ton hnh thi hc ng nh ................. 74

Hnh 3.10: Xc nh hnh ch nht v elip bao quanh i tng. .................... 75

Hnh 3.11: thng k t l khung ca trng thi ng ........................................ 75

Hnh 3.12: thng k t l khung ca trng thi ngi ......................................... 76

Hnh 3.13: thng k t l khung ca trng thi ng........................................... 76

Hnh 3.14: thng k gc trng thi ng ......................................................... 76

Hnh 3.15: thng k gc trng thi ngi........................................................... 77

Hnh 3.16 Thng k gc trng thi ng ............................................................ 77

Hnh 3.17: Cc mc ngng nhn din cc trng thi ..................................... 78

Hnh 3.18: Cnh bo trn mn hnh ................................................................. 78

Hnh 3.19: Giao din bo ng ....................................................................... 80

Hnh 3.20: Giao din bo ngi ........................................................................ 81

Hnh 3.21 Giao din cnh bo ng ................................................................... 81

........................................................................................................................ 82


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Hnh 3.22: Giao din bo ng .......................................................................... 82

Hnh 3.23: Th nghim ngoi tri sn D4 i hc Bch Khoa H Ni ......... 83

Hnh 3.24: Th nghim ngoi tri sn thng gia nh ................................... 83

Hnh 3.25: Th nghim hnh lang ti snh D4, H BK HN ......................... 84

Hnh 3.26: Th nghim trong nh ................................................................... 85

Hnh 3.27: Th nghim trong nh ................................................................... 85


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Danh sch cc bng biu

Bng 1.1. Thng tin ca i tng th nghim ................................................ 17

Bng 1.2. T l pht hin t ng ca bn loi ng khc nhau. ........................... 17

Bng 1.3. T l khng nh sai ca cc hnh ng ......................................... 18

Bng 1.4: Bng thng k chnh xc ca cc c tnh ng ............................ 29

Bng 3.1: Cc khi nim ................................................................................. 86

Bng 3.2: Thng k th nghim ...................................................................... 86


8

Phn m u

Tui th cao i i vi chm sc sc khe tng cao. Vic ko gim nhng yu

t nguy c d gy tai nn cho ngi cao tui l ht sc quan trng. Nu nh chng

ta cho rng tui gi l mt thch thc ca nhn loi th tai nn t ng ngi gi l

mt thch thc to ln. Mi nm, trn th gii c n 1/3 n dn s tui trn

65 b ng. Trong s , mt na b ng ti din nhiu ln. Ng l nguyn nhn hng

u cho cc chn thng ca ngi gi v cng l nguyn nhn ch yu dn ti cc

ca t vong do ti nn ca ngi gi trn 75 tui, c tnh khong 70% s cc ca t

vong do tai nn. Hn 90% cc ca gy xng chu xy ra do b ng, vi hu ht cc

ca gy xng u xy ra ngi trn 70 tui. V ti Vit Nam, c tnh c khong

1,5-1,9 triu ngi cao tui t ng mi nm. Vi s pht trin ca x hi, khi nhng

ngi tr khng c thi gian chm sc cho ngi gi th nhng tai nn v ng s

tip tc tng trong nhng nm tip theo.

Nhiu ngi cao tui sng mt mnh do con ci i xa hoc phi nh mt mnh

khi con h i lm. C rt nhiu trng hp ngi cao tui sau khi ng th khng th

t ng ln hoc gi c s gip t ngi khc. V vy, trn th gii c rt

nhiu nh sn xut a ra sn phm pht hin t ng dnh cho ngi cao tui.

Nhim v trc tin t ra ca mt h thng pht hin ngi gi ng l h c th

qua gi c s gip ngay c khi ri vo tnh trng v thc hoc khng

th t ng dy sau khi ng.

pht hin ngi b ng mt cch chnh xc nht cn mt h thng s dng

ng thi hai thit b l cm bin gia tc gn trn ngi v camera quan st kt ni

vi my tnh gn trong phng ca ngi gi. H thng s dng cc thut ton phn

tch da trn cc d liu nhn c t cm bin v camera ng thi nhn bit cc

trng thi ng v khng ng. Nu pht hin ngi b ng sau mt khong thi

gian nht nh khng thy ng ln h thng s a ra cnh bo cho ngi s dng

l ngi thn hoc bc s, chm sc vin

Hin ti, Vit Nam c rt t cc sn phm h tr pht hin t ng . Sau khi

nghin cu cc phng php pht hin t ng trn th gii v nhn thy tm quan


9

trng ca vic a ra thit b pht hin ng, nhm la chn ti Pht hin t

ng ca ngi gi s dng camera quan st lm n tt nghip.

H thng pht hin ngi b ng s dng camera quan st c gn trong khu

vc ngi gi sinh hot. N s gi cho my tnh hnh nh ngi gi theo thi gian

thc. My tnh s s dng cc thut ton x l hnh nh xc nh c ngi gi

c b ng hay khng.

n c chia lm cc chng nh sau:

Chng 1: Tng quan v cc phng php pht hin t ng. Chng ny

a ra mt s phng php pht hin t ng c ng dng trn th gii. ng

thi a ra cc u, nhc im ca cc phng php . T a ra phng php

pht hin t ng c s dng trong ti ca nhm.

Chng 2: C s l thuyt. Chng ny a ra cc c s l thuyt chung

c s dng lm ti ca nhm.

Chng 3: Xy dng h thng pht hin t ng ngi cao tui. Chng

ny gii thiu cc cng c s dng thc hin ti, a ra cc thut ton p

dng xy dng c chng trnh pht hin t ng, cui cng l trnh by kt

qu thu c ca qu trnh nghin cu v thc hin ti.

Chng 4: a ra cc Kt lun cng vic hon thnh, cc hn ch v d

nh nghin cu trong tng lai ca nhm.


10

Chng 1: Tng quan vn pht hin t ng nhng ngi

cao tui.

1.1. Thc trng vn ng ngi cao tui

Ng l mt nguy c ln nh hng n sc khe, lm gim cht lng cuc

sng nhng ngi cao tui. Mi nm, trn th gii c t 1/3 n 1/2 dn s

tui trn 65 b ng. Trong s , mt na b ng ti din nhiu ln. Ng l nguyn

nhn hng u ca cc chn thngv cng l nguyn nhn ch yu dn ti cc ca

t vong do ti nn ngi gi trn 75 tui, con s c tnh khong 70%. Hn 90%

cc ca gy xng chu xy ra do b ng, v hu ht u xy ra ngi trn 70

tui[1][2].

Vi s pht trin ca x hi,

khi nhng ngi tr khng c

thi gian chm sc cho ng

b, cha m, nhiu ngi cao tui

phi sng mt mnh do con ci

i xa hoc nh mt mnh khi

con h i lm. C rt nhiu

trng hp ngi cao tui sau khi ng th khng th t ng ln hoc khng th gi

s gip t ngi khc. Cn vi nhng ngi gi trong nh dng lo, cc nh

nghin cu c tnh c n 50% ngi gi ng mi nm v hn 40% trong s h

ng nhiu ln[3]. Khng ch gy ra nhng vt thng th xc nh gy xng, trt

[1] Koen Milisen, Els Detroch, Kim Bellens, Tom Braes, Katrien Dierickx, Willy Smeulders, Stefan Teughels,

Eddy Dejaeger, Steven Boonen, and Walter Pelemans, Falls among community-dwelling elderly: a pilot

study of prevalence, circumstances and consequences in flanders, Tijdschr Gerontol Geriatr, vol. 35, no. 1,

pp. 15 20, 2004.

[2] M. E. Tinetti, Preventing falls in elderly persons, New England Journal of Medicine, vol. 348, no. 1, pp.

42 49, 2003, 57 MASSACHUSETTS MEDICAL SOC/NEJM WALTHAM 630WY.

[3] P. A. Stalenhoef, J. P. M. Diederiks, J. A. Knottnerus, A. D. M. Kester, and Hfjm Crebolder, A risk

model for the prediction of recurrent falls in community-dwelling elderly: A prospective cohort study,

Journal of Clinical Epidemiology, vol. 55, no. 11, pp. 1088 1094, 2002, 39 PERGAMON-ELSEVIER


11

khp, tn thng no b,...t ng cn gy hu qu nghim trng v tm l lm gim

cuc sng c lp ca ngi cao tui.

Vi cc s liu nghin cu hng nm c a ra ngn gn trn, cng vic gia

tng nhu cu chm sc sc khe, t bit l sc khe ngi cao tui, cc sn phm

nh Pht hin t ng ang ngy mt tr nn cn thit. Hin nay trn th gii c

rt nhiu cc cng trnh nghin cu v cc sn phm thng mi lin quan n lnh

vc ny.

1.2. Vai tr ca h thng pht hin t ng ngi ln tui

Mt h thng pht hin t ng phi m bo cc yu t sau:

Lun theo st cc hot ng ca ngi cao tui.

Pht hin chnh xc tnh trng ca ngi gi, in hnh h thng ny l

hai trng thi Ng v Khng ng.

a ra cnh bo kp thi.

S dng mt h thng pht hin t ng s mang li cc li ch:

i vi ngi cao tui:

o Kp thi can thip vo nhng tnh hung khn cp.

o Thay ngi gi a ra li gi gip khi h khng th t mnh

lm iu .

o To cm gic an ton, tm l thoi mi khi s dng.

i vi ngi tr:

o D khng phi l s thay th hon ton cho vic chm sc ngi

cao tui, nhng y l mt gii php tng tnh an ton khi

nhng ngi con, chu khng th dnh s theo di trc tip, lin

tc ti ng b, cha m.

o To cm gic yn tm khi lun theo st v gii quyt c nhng

tnh hung khn cp ca ngi cao tui.

SCIENCE LTD OXFORD 628TM.


12

1.3. Cc h thng Pht hin t ng c trn th gii

1.3.1. Tng quan cc h thng hin c

Hin nay trn th gii, cc dng sn phm chm sc sc khe con ngi ni

chung v dng sn phm Pht hin t ng ni ring ang rt pht trin v c ng

dng rng ri. in hnh c th k ti cc sn phm sau:

Mt s sn phm pht hin t ng:

Fall detector ZigBee

Blue Alert Fall Detection Sensor[4]

Cc dng sn phm Pht hin t ng c chia ra lm 2 nhm chnh: Nhm sn

phm s dng cm bin gn trn ngi v nhm sn phm s dng camera theo di.

[4]http://www.bluealertalarm.com/index.cfm?page=equipment


13

Thit b Pht hin t ng s dng cm bin c eo trn c th ngi cao tui-

y thng s dng cm bin gia tc - pht hin hng, ln gia tc dc theo

mt trc nht nh - thng l ba trc x, y, z,t c th tnh ton c gc ca

mt ngi so vi mt t v pht hin ngi b ng hay khng. Thit b s t

ng gi gip khi ngi ng hoc khi xut hin mt khong thi gian bt

thng khng c chuyn ng cho thy c th l mt trng thi khn cp. Bn

cnh , nh mt thit b y t, sn phm c mt nt gi s gip t ngi thn

ging nh nt nhn SOS trn in thoi ngi gi c mt s nh mng vin

thng Vit Nam ng dng trn sn phm ca h.

Thit b Pht hin t ng s dng camera theo di li thng c ng dng

trong nh.Camera c gn trong khu vc sinh hot, chu trch nhim ghi li hnh

nh theo thi gian thc v gi cho my tnh x l nhm phn loi ngi gi ng hay

khng. Vi sn phm ny chi ph s thp hn, thm vo ngi cao tui khng

phi vng bn vic qun mang theo bn ngi nh khi s dng cm bin, d nhin

chiu ngc, nhc im ca n li chnh l ch s dng c trong mt phm vi

nht nh.

Nu trn l hai nhm sn phm ang c ng dng v nghin cu trn th gii,

bn cnh nu s dng ng thi cm bin gn trn ngi v camera theo di

Pht hin t ng, chc chn kt qu s chnh xc hn. l s kt hp tt nht

gii quyt nhng trng hp ngi gi i vo khu vc camera khng quan st c

th vn c th xc nh trng thi thng qua cm bin. Tuy nhin vi h thng ny

chi ph s cao hn nhiu, ng thi kh nng ti u thng mi s gp nhiu tr

ngi. Di y l s khi chc nng ca h thng pht hin t ng da vo cm

bin gia tc v camera quan st.


14

Hnh 1.1: S khi tng quan h thng kt hp cm bin v camera

Nu trn l tng quan v cc h thng Pht hin t ng c nghin cu v

thc hin trn th gii. Cc phng php v sn phm c th s c trnh by

phn di y.

Nng cao cht lng nh

nh du v tr ngi gi

Nhn bit hnh dng, din tch

ngi gi

So snh vi cc hnh dng, din

tch lu

Tng hp

Xc nh gc ca ngi theo phng thng ng

So snh vi gc c quy nh l ng

m thi gian khng tr v trng thi Khng ng

Qu thi gian truyn tn hiu n

b thu

Kt qu

Hnh nh t Camera

D liu t cm bin


15

1.3.2. Thit b pht hin t ng s dng cm bin

Trn th gii, cc thit b Pht hin t ng s dng cm bin tr nn rt thng

dng. Sau y l hai sn phm ni bt trong nhm ny: chic gy SmartFall v my

h tr Vector.

a) SmartFall

Gii thiu

Cc nh nghin cu ch ra rng: chn yu, ri lon dng i, ri lon cn bng

l nguyn nhn chnh ca cc s c t ng. Phn ln ngi cao tui s dng gy

khc phc cc vn ny. Sn phm SmartFall c pht trin t nn tng trc

: SmartCane - mt chic gy c thit k hin i, bao gm mt b cm bin v

mt b pht tn hiu wireless.D liu cm bin s c chuyn ti mt thit b c

nhn xa. Mc tiu ca h thng ny l t ng cnh bo khn cp cho ngi

thn khi ngi s dng gy ng v khng th gi s gip .

Cu trc h thng

Hnh 1.2: M t h thng SmartCane

M t Hnh 1.2: H thng SmartCane bao gm ba thnh phn c bn:

Mt b cm bin (sensors) vi tn hiu u ra l tn hiu biu th chuyn

ng, lc, v p lc.


16

Mt b phn thu nhn tn hiu t b cm bin(acquisition unit) v giao tip

vi cc thit b bn ngoi thng qua mt lin kt khng dy wireless.

Mt thit b c nhn (personal device)thu thp v x l d liu c gi t

b phn thu nhn tn hiu t b cm bin.

Din gii Hnh 1.2:

Cm bin trn gy SmartCane bao gm mt gia tc k ba trc, ba trc con quay

hi chuyn tn hiu v hai cm bin p lc. Cc con quay hi chuyn t vung gc

vi nhau o tc gc trong khng gian ba chiu, v gia tc k c gn gn tay

cm ca gy vi gc nghing 30 t hng ca trng lc. Hai cm bin p lc c

gn vo tay cm v u gy. Ngoi ra nh sn xut cn kt hp thm cm bin o

vn tc ri ca gy tng tnh chnh xc khi phn bit cc hot ng bnh thng

vi t ng.

B phn thu nhn tn hiu bao gm mt b x l MicroLEAP bng Bluetooth

trn b mt. Mi knh u vo cm bin c th ly mu ti mt t l ln n 300Hz.

i vi ng dng SmartCane, ly mu c chn 26Hz. n v ny rt hiu qu

trong vic h tr ly mu lin tip trong hn 20 gi. Bluetooth truyn ti d liu

bng cch s dng 6 pin c AA-2200mAh.

Thit b c nhn c th l thit b di ng bt k m c h tr Bluetooth. y,

chn mt my tnh bng cho d dng lp trnh v hin th d liu, thut ton c th

d dng c gi ti in thoi di ng hoc PDA. Tn hiu gi n c nhn v

ghp thnh mt file. Phn mm SmartFall sau c trc tip t file v thc hin

cc thut ton pht hin trong mt thi gian thc gn nht.

Kt qu

Hiu qu ca SmartFall c o thng qua mt lot cc th nghim. Nhng th

nghim ny c xy dng nh gi t l pht hin t ng (khng nh ng) v

kh nng phn bit cc hot ng trong cuc sng khng phi l t ng (khng nh

sai).

Cc nh sn xut chn ba i tng khm sc khe thc hin th nghim.

H s ca h c th hin trong bng 1.1. Cc i tng c chn nghin cu

kh nng nh hng ca trng lng v chiu cao tikt qu cui cng.


17

Bng 1.1. Thng tin ca i tng th nghim

i tng 1 2 3

Tui 35 28 25

Gii tnh Nam Nam N

Chiu cao 1,86 m 1,60 m 1,65 m

Cn nng 109,3 kg 63,5 kg 50,8 kg

Cc kt qu pht hin t ng

Sau y l bn loi t ng c th nghim nh gi t l pht hin ca

SmartFall:

Ng v pha trc: ng xung do vp, hoc sy chn.

Ng v ng sau: ng do trt chn.

Ng v mt bn: ng do mt cn bng.

Ng t do: ng khng c vt cn tr do mt kim sot.

Mi loi ng c thc hin 30 ln. Cc kt qu c lit k trong Bng 1.2 cho

thy mt t l pht hin gn 100% cho bn loi ng c thc hin bi ba i tng,

s khc bit v trng lng v chiu cao gia cc i tng t nh hng n kt

qu cui cng.

Bng 1.2. T l pht hin t ng ca bn loi ng khc nhau.

1 2 3

V pha trc 100% (30/0) 100% (30/0) 100% (30/0)

V ng sau 100% (30/0) 100% (30/0) 93,3% (28/2)

V mt bn 100% (30/0) 96,7% (29/1) 100% (30/0)

T do 100% (30/0) 100% (30/0) 100% (30/0)

Phn bit cc trng thi ng t hot ng hng ngy l rt quan trng. Thng

xuyn bo ng nhm c th lm ngi dng khng sn sng chp nhn h thng.

Sc mnh phn bit ny ca SmartFall c nh gi bng cch s dng su th

nghim khc nhau th hin su hnh ng thng c thc hin cng gy:

i chm: i b vi gy tc nh hn mt bc/giy.


18

i nhanh: i b vi gy tc khong 2 bc/giy.

Ngi v ng: ng ln vi s gip ca gy t trng thi ngi.

ng: Vn ng yn vi mt phn nh trng lng dn vo gy.

Rung gy: ong a gy vi tn sut khong 1Hz vi mt gc nh hn 30

t trc thng ng.

t ln i: cm gy ang hng thng ng v t trn i khi ngi.

Tt c cc trng hp(tr ng) vn c thc hin 30 ln vi c 3 i tng

th nghim. Ring trng hp ng c th nghim trong 30 giy ng yn. T l

khng nh saic th hin trong Bng 1.3.

Bng 1.3. T l khng nh sai ca cc hnh ng

1 2 3

i chm 0% (0) 0% (0) 3,3 % (1)

i nhanh 0% (0) 0% (0) 0% (0)

Ngi v ng 0% (5) 0% (1) 0% (0)

ng 0 trong 30 giy 0 trong 30 giy 0 trong 30 giy

Rung gy 0% (0) 0% (0) 0% (0)

t ln i 10% (3) 3,3% (1) 3,3% (1)

u im:

SmartFall khng yu cu ngi s dng mc hoc eo dy cm bin trc tip

vo c th.

N l hnh nh thu nh ca mt h thng y t khng dy (PNI).

SmartFall c 1 t l pht hin t ng cao hn v chi ph thp hn so vi cc

hnh thc pht hin t ng PNI khc. V d nh: dy cm bin tia hng ngoi,

cm bin rung, h thng phn tch m thanh-hnh nh,...

Quan trng hn, h thng SmartFall khng yu cu thit lp cc khu vc

gim st c bit v do t b nh hng bi s di ng ca ngi dng.

Kt lun

Trn y l chic gy SmartFall, mt thit b t ng pht hin t ng, pht trin

da trn nn tng SmartCane.SmartFall s dng b cm bin gia tc v b pht


19

bluetooth pht hin t ng. Mt s th nghim m phng t ng c thc

hin nh gi hiu qu ca SmartFall, kt qu ch ra rng cc thut ton c th

pht hin hu ht cc trng hp t ng v t c t l bo ng sai rt thp.

b) My h tr vector

Gii thiu

Trong phng php ny, vic pht hin t ng ca ngi cao tui da trn cc

d liu ca my o gia tc. B cm bin thu thp cc d liu chuyn ng v truyn

ti n n v gim st thng qua ng truyn khng dy. Vic phn loi d liu

c thc hin bi my h tr vector, c th phn loi cc hot ng thanh ba loi:

ng, i b v chy. Sau , vic xc nhtrng thi da trn cc hot ng trc

c th cho php m ha v truyn ti hnh nh video t bnh nhn n cc n v

gim st t xa.

Cu trc h thng

Hnh 1.3. Cu trc h thng, tng tc d liu gia cc nt cm bin

v nt gim st

M t Hnh 1.3: D liu gia tc c thu thp thng qua cc cm bin gn trn

chn ca ngi s dng v c truyn qua ng truyn khng dy ti nt gim


20

st. D liu c chuyn i sang cc nh dng c nh ngha t trc, t

s pht hin c tnh trng ca bnh nhn. Mt mng li gim st module xc

nh cht lng c s h tng mng c bn s quyt nh cc m ha thch hp v

truyn ti hnh nh bnh nhn bng cch s dng H.263 nn video.

Kt qu

nh gi hiu qu v tnh chnh xc ca h thng, ba th nghim khc nhau

c thc hin vi hai tnh nguyn vin:

i b thng.

i b thng v ng.

i b thng v chy.

H thng s phn tch d liu o gia tc c c. Mi trng hp tng tc, gi

tr trn cc trc X, Y, Z c xc nhn thi gian chy v mt loi chuyn ng

tng ng c lin kt vi n. Cn c vo s lng xut hin tun t ca mt

loi chuyn ng c th, quyt nh v t ng ca bnh nhn c thc hin.

tng tnh chnh xc ca quyt nh, b lc Kalman c s dng trong h thng.

Kt qu phn loi t cc th nghim c tin hnh bng cch s dng m hnh

My h tr Vector. B lc Kalman ci thin vic pht hin bng cch lm mn s

xut hin tnh hung Chy hoc cc s c Ng. S dng My h tr Vector th cc

s c Ng c pht hin vi chnh xc trung bnh 98.2%, trong khi tnh

hung Chy c pht hin vi 96.72%.


21

Hnh 1.4. Kt qu phn loi da trn m hnh SVM

cho 3 loi chuyn ng khc nhau (a) i b thng, (b) i b thng v ng,

(c) i b v chy.

Kt lun

Vi phng php ny, bnh nhn c theo di bng thit b cm bin gia tc

trn trc X, Y, Z. Phn loi d liu da trn My h tr Vector pht hin cc s

c t ng vi chnh xc ln n 98.2%. V theo nh nh gi ca cc nh chuyn


22

mn, trong tng lai, cn tng cng pht hin t ng bng cch s dng camera

ca my tnh, vic ny s mang li kt qu cn chnh xc hn.

1.3.3. Thit b pht hin t ng s dng camera

phn ny s gii thiu cc thit b Pht hin t ng s dng hay ni cch

khc l s dng h thng Th gic my (Computer Vision). Di y l hai cng

trnh nghin cu in hnh trong lnh vc ny: Phng php theo di qu o u

ngi [1] v Pht hin t ng s dng camera n [2].

a) Phng php thc hin da trn qu o 3D ca u ngi

Gii thiu

Phng php ny thc hin bng cch s dng d liu 3D qu o u ngi,

cho php theo di chuyn ng ca s t ng v phn bit c vic t ng t cc

hnh ng bnh thng.

Cu trc thut ton

Phng php thc hin da trn ba bc:

Head Tracking: theo di v tr ca u b phn c th lun nhn thy trn

khung hnh v s chuyn ng nhanh trong qu trnh ng.

3D Tracking: u ngi c theo di bng b lc ring phn Particle

Filter [3] tch ra mt qu o 3D.

[1] C. Rougier, J. Meunier, A. St-Arnaud and J. Rousseau, 3D Trajectory to Detect Falls of

the Elderly Using a Monocular Camera, International Conference of the IEEE

Engineering in Medicine and Biology Society, New York, September 2006 (4 pages -

submitted).

[2] Glen DEBARD, Peter KA RSMAKERS, Mieke DESCHODT, Ellen VLA EYEN, Jonas VAN DEN

BERGH, Eddy DEJAEGER, Koen MILISEN, Toon GOEDEM, Tinne TUYTELAARS, Bart

VANRUMSTE, CAMERA BASED FALL DETECTION USING REAL-LIFE VIDEO, MOBILAB:

Biosciences and Technology Department, K.H.Kempen, Belgium; Center for Health Services and Nursing

Research, K.U.Leuven, Belgium; Lessius, Campus De Nayer, Belgium; U.Z.Leuven, Belgium; ESAT-PSI,

K.U.Leuven, Belgium; ESAT-SCD, K.U.Leuven, Belgium

[3] M. Isard and A. Blake, Condensation conditional density propagation for visual

tracking, in International Journal of Computer Vision, vol. 29, no. 1, 1998, pp. 5-28.


23

Fall Detection: Qu trnh t ng c pht hin bng cch s dng vn tc

3D c tnh ton t qu o 3D ca u ngi.

Cch xc nh v tr ca u (Head Localization): u ngi c dng hnh

tri xoan trong mt phng 3D v c hnh elip trong mt phng 2D. u ngi c

xc nh bng thut ton Dementhon [4], n c ba thng s u vo: cc im 3D

ca m hnh u, cc im 2D tng ng trong nh v cc thng s ni ti ca

camera. u ra ca thut ton dng 3D trong h ta ca my nh, c th

chuyn i trong h ta chun gn lin vi mt phng t XY. phc hi cc

qu o 3D s dng mt b lc ring phn.

Theo di 3D: Li th ca cc b lc ring phn l chng cho php s thay i

t ngt trn qu v c th i ph vi nhng li nh. Phng php thng

thng theo di du l s dng nhng b lc ring phn lm vic tt vi nhng

chuyn ng nh. Trong trng hp ny, chuyn ng c th rt ln khi ng, do

vy cn iu chnh phng php hin ti d gii quyt vn ny. B lc ring

phn c iu chnh thnh ba bc. u tin mt b lc ring phn tm ln cn

ca v tr cui cng hnh elip. Nu n khng tm thy u, mt b lc th hai c

s dng tm kim v tr gn ng ca u ngi trong nh mi v b lc ring

phn th ba lc ra cc v tr. Cc b lc ring phn c da trn mc xm xung

quanh chu vi hnh elip v kh nng thay i mu nn trong .

Pht hin t ng:Tin hnh ly mu [5] s dng vector Vv theo phng thng

ng v vector Vh theo phng nm ngang trong h ta chun phn bit Ng

vi cc hot ng bnh thng. Qu o 3D c trch xut t tn hiu video c

s dng a ra nhng nt c trng. Bng cch ly ngng vector Vv v Vh c

th xc nh Ng. Mt v d v vn tc thu c t mt qu o 3D c hin th

trong Hnh 1.5.

[4] D.F. Dementhon and L.S. Davis, Model-based object pose in 25 lines of code,

International Journal of Computer Vision, vol. 15, no. 1-2, June 1995, pp. 123-141.

[5] G. Wu, Distinguishing fall activities from normal activities by velocity characteristics,

in Journal of Biomechanics, vol. 33, no. 11, 2000, pp. 1497-1500.


24

Hnh 1.5. Minh ha vector Vv v Vh.

Cc hnh ng ca ngi (a) ng ln, (b) ngi xung, (c) ang ngi, (d) ng

ln ln na, (e) ng, (f) ng.

H thng pht hin ng bao gm mt camera duy nht c t trong mt gc

trn ca cn phng. gim chi ph, h thng c xy dng da trn mt webcam

USB c gc quan st rng khong 70 quan st ton b cn phng. Camera

ny cho cht lng hnh nh thp v nh b bin dng do gc rng.

Kt lun

Vi h thng ny, chng ta xy dng c mt tp hp cc video vi cc tnh

hung t ng v cc tnh hung bnh thng nh ang ngi xung hoc ng. Hnh

nh c phn gii 640x480 pixel v tc 30 khung hnh/s. Cc biu hin s a

ra nhng kt qu theo di v tr u trong video v a ra mt s kt qu pht hin

ng s dng cc c tnh vn tc.

Vector vn tc 3D ly ra t mt camera cung cp mt phng php pht hin t

ng mi. Vic theo di u ngi cung cp cho ta mt qu o 3D, hu dng khi

phn bit ng vi cc tnh hung bnh thng. Tuy nhin vn cn mt s im cn

phi c ci thin. Hin nay, cc hnh elip i din cho u ngi trong hnh nh

u tin l phi t khi to, do cn pht trin mt phng php pht hin u

ngi t ng khi c ngi bc vo phng. Tng cng h thng nhn bit ng.


25

b) Pht hin t ng s dng mt camera quan st

Gii thiu

Phng php ny s dng mt camera duy nht da trn nn tng h thng pht

hin t ng trn nhng video thi gian thc.

Thut ton pht hin t ng ca phng php ny gm bn phn chnh: thu thp

video, theo di i tng trong video (Person Tracking), pht hin t ng (Fall

Detection) v pht tn hiu cnh bo (Alarm Generation). on video c chuyn

i sang hnh nh mc xm, cch ny khng cn phi thay i qu trnh x l nu

chuyn sang s dng ch camera hng ngoi vo ban m. Vic pht tn hiu

cnh bo khng thc hin trong lc ny.

Cu trc h thng

Hnh 1.6. Tng quan v thut ton pht hin t ng

M t Hnh 1.6:

Theo di i tng (Person Tracking):

Xc nh i tng (Foreground Detection):

u tin cn bit vng nn ca nh, lm c iu ny cn s dng k thut

tr nn da trn b lc trung bnh. L thuyt c pht trin nm 1995 bi


26

McFarlane v Schofield nhm theo di n ln [6]. K thut ny s dng vic d

on vng nn. Vng c d on s c so snh tng pixel ca khung hnh hin

ti vi nhng khung hnh c cp nht sau . Trong trng hp pixel khung

hnh hin ti sng hn pixel vng nn, vng nn s c ln thm pixel v

ngc li.

i tng c th c xc nh bng cch tnh ton s khc bit gia khung

hnh hin ti v vng nn. Trong trng hp n ln hn mt ngng nht nh,

pixel l pixel i tng (foreground). Tri li, n l pixel nn (background). Li

ch ca b lc trung bnh l tiu th t b nh, tc tnh ton nhanh v chnh

xc,hn ch l cp nht chm vi nhng thay i nh sng ln, trn thc t th i

tng nh hng ng k n vng nn khi n xut hin. V d khi mt ngi ngi

trn gh salon trong mt khong thoi gian di, vng nn s c cp nht kt

hp vi ngi vo. Nu ngi ng dy, vng hnh nh gh salon b che

khuttrc s khc vi vng nn v c pht hin ra l i tng trong khung

hnh. iu ny c th nh hng ti vic pht hin t ng sau ny.

Loi b bng (Shadow Removal):

Mt vng ti gy ra bi mt i tng di chuyn cng c th c pht hin

nh mt i tng v n lm cc im nh bao ph quanh i tng ti hn. iu

ny lm cho i tng trong khung hnh sai lch. loi b bng, c th s dng

thuc tnh cabng: ch l s thay i cng sng ca pixel, cu trc ca vng

bao ph khng thay i [7]. Do cu trc ca bngs tng quan vi cu trc ca

hnh nh vng nn.

Jacques et al. m t trong [4] cch s dng tng quan cho (Cross Correlation)

xem chnh xc ca nhng pixel i tng c pht hin c ph hp vi

nhng pixel vng nn.

[6] N. J. B. McFarlane and C. P. Schofield, Segmentation and tracking of piglets in

images, Machine Vision and Applications, vol. 8, no. 3, pp. 187.193, May 1995.

[7] Daniel Grest, Jan-Michael Frahm, and Reinhard Koch, A color similarity measure for

robust shadow removal in realtime, Vision, Modeling and Vizualization, 2003.


27

Xc nh vng cn quan tm (Region of Interest (ROI) Detection):

Bc tip theo trong thut ton ny l xc nh vng cn quan tm xc nh

con ngi trong khung hnh. Trc tin lm n mn ri m rng cc pixel c cho

l i tng. Sau phn tch cc thnh phn kt ni nhm xc nh tng vng.

Vng ln nht s c nh ngha l con ngi. gim thiu ti a nhiu, i

tng con ngi phi ln hn mt ngng nht nh, v thut ton ny mc

ngng c hiu sut tt nht l 17500 im nh. Cui cng t i tng ny, chng

ta bt u trch xut ra cc c im pht hin t ng.

Nhng c tnh xc nh ng (Fall Detection Features)

Vi i tng c trch xut ra saucc bc trn, c th dn ra bn c im

pht hin t ng: t l khung hnh ch nht gii hn (aspect ratio), gc ng (fall

angle), tc di chuyn ca trng im (speed center gravity) v tc di chuyn

ca u (speed head).

T l ca khung gii hn c tnh ton bng cng thc chia chiu rng ca

khung gii hn cho chiu cao. Mt t l nh i din cho mt ngi ang ng

thng, cn khi t l ny ln th hin mt ngi ang nm xung.

Gc ca ngi trong hnh c th nh ngha l gc gia trc di ca hnh elip

gii hn v mt phng nm ngang ca hnh nh. gc nhn ngang, mt ngi ang

ng c gc l 90,cn khi gc gn bng 0 i din cho mt ngi ang nm

xung. Gc ng l s sai lch gia gc trong khung hnh hin ti v gc c xc

nh trc . Thut ton ny s dng 2 giy gia nhng khung hnh o gc.

Gc ng m gn bng 90 c th l mt t ng.


28

Hnh 1.7. M t hnh elip bao quanh ngi

Mt ngi, quan tm y l mt ngi gi, thng di chuyn vi tc thp,

trong khi tri ngc li hu ht cc t ng xy ra s chuyn ng vi tc cao.

Thut ton ny chia ra lm hai khc bit tc . Tc di chuyn ca trng tm v

tc di chuyn ca u. Trng tm c th c li th l n kh n nh. Nhng hot

ng bnh thng ca con ngi ch mang li nhng thay i nh i vi im ny.

u ngi c th nhn thy trong hu ht cc trng hp. Foroughi et al. m t

trong [5] rng u ngi l im cao nht ca i tng. Thut ton ny s dng

im cao nht ca trc di hnh elip lm v tr ca u ngi. Tc ca im u

c xc nh bng s lng im nh m im u dch chuyn gia hai khung

hnh lin k chia cho thi gian gia hai khung hnh .

D liu th nghim

Trong sut qu trnh nghin cu, d n ghi li 25 tnh hung ng trong hin

thc. phn gii ca cc bc nh l 640x480 pixels. Vi mi tnh hung trong s

22 trng hp t ng khc nhau, tc gi to ra mt video di 20 pht c tnh hung

ng xy ra phn cui ca video.

Trong qu trnh thit lp d liu, nhiu bi kim tra c thc hin. u tin

cc c im khc nhau c nh gi ring bit. Trong trng hp ny, gi tr

ngng b thay i nhm t ti hiu sut cao nht cho mi c tnh. Sau , tc gi

tin hnh thm nh bng vic s dng mt My H tr Vector t ng a ra

gii php kt hp ti u cc c tnh trn.

Kt qu - Kt lun

u tin cc c im khc nhau k trn c x l ring bit. Vi mi c

im tc gi nghin cu cho ra mt ngng c hiu sut cao nht

Bng di y cho ta thy t l khung v c tc trng tm ln u u cho

kt qu tng t nhau. Sensitivity vo khong 0.72 0.80, nn 72 80% s t ng

c pht hin. Gi tr d on tch cc (PPV) kh thp 0.17, ngha l khong 1/5

l tnh hung ng thc s. Da vo gc ng to ra mt s lng ln cc pht hin

sai.


29

Bng 1.4: Bng thng k chnh xc ca cc c tnh ng

PPV: Positive Predictive Value Gi tr d on tch cc.

Sai Tch cc: nu i tng hot ng bnh thng m h thng li cnh bo

t

Sai Tiu cc: c xy ra tai nn t ng m h thng khng pht hin v khng

cnh bo l sai tiu cc.

Thut ton trn cho ta mt phng php xc nh t ng ca i tng trong

mt on video thi gian thc. Cn pht trin thm thut ton xc nh i tng v

xy dng thm nhng c tnh pht hin t ng nhm c c chnh xc cao

nht. D n ny mi ch gii thiu v hiu sut ca thut ton i vi mt c tnh

ring l v s kt hp khi s dng cng vi mt SVM, qua kt lun rng mt

SVM s dng t l khung gii hn v tc u ca i tng ln nht s cho kt

qu tt nht.

Tc gi a ra nhim v cn ci thin hiu sut pht hin i tng - s dng

mt thut ton khc c kh nng nhn din nhiu i tng xut hin trong khung

hnh.

c

im Ngng

ng

tch

cc

ng

tiu

cc

Sai

tch

cc

Sai

tiu

cc

Sensitivity Specificity PPV

T l

khung 2.5 18 250 85 7 0.72 0.746 0.174

Gc

ng 80 22 86 248 3 0.88 0.257 0.081

Tc

trng

tm

180 19 247 87 6 0.76 0.739 0.179

Tc

im

u

640 20 241 93 5 0.80 0.722 0.177


30

1.3.4. So snh cc h thng hin c

phn ny s a ra nhng u Nhc im ca hai nhm thit b Pht hin t

ng c cp trn. y s l c s nhm chng em khoanh vng phm vi v

thit k sn phm trong n tt nghip.

a) u nhc im ca h thng Pht hin t ng s dng cm bin

u im

Thit b nh gn, d s dng.

S dng cm bin cho chnh xc trong vic xc nh t ng cao.

Nhc im

Nhng thit b ny i hi ngi s dng phi lun ghi nh gn trn ngi.

Trong cuc sng hng ngy s khng th trnh khi vic ngi s dng c

th qun mang theo thit b.

b) u nhc im ca h thng Pht hin t ng s dng camera

u im

Mt h thng c chi ph r, d lp t, thn thin vi ngi s dng.

Mt h thng t ng khng cn bt c s tc ng no ca ngi s dng

trong qu trnh vn hnh.

Vic ng dng Th gic my vo Chm sc sc khe s m ra mt hng

pht trin mi, tch hp c nhiu cng dng ch trong mt thit b camera.

Nhc im

Phm vi theo di ca thit b hp, mi cn phng cn c lp t mt

camera c gc m rng mi c th bao qut khp phng.

Vic s dng camera gc m rng i khi dn ti mo dng hnh nh, dn

ti sai lch, tuy nhin y l nhc im c th khc phc nh vo vic x l

nh.

Cha c tnh ng dng i vi vic ngi gi i ra ngoi.

c) Kt lun nh gi

Trong x hi hin nay, khi cng ngh ngy mt pht trin mnh m th yu cu

v tnh t ng v tnh chnh xc lun lun l u tin hng u ca mi sn phm.


31

Trong lnh vc Pht hin t ng, im khc bit ln nht gia hai loi thit b s

dng cm bin v s dng camera theo di l tnh t ng. Vi Camera theo di,

mi hot ng ca con ngi s c ghi li, x l, phn loi v a ra cnh bo

mt cch hon ton t ng, trong khi thit b s dng cm bin vn cn phi

c s can thip ca ngi s dng khi phi lun mang theo trn ngi. Tuy nhin

tnh chnh xc ca camera theo di trong vic phn loi trng thi cn cn phi th

nghim rt nhiu trc khi c th tr thnh mt sn phm thng mi nh cc thit

b s dng cm bin lm c.

Ngy nay, song hnh cng cm bin, cc thit b s dng camera ni chung ang

l xu hng pht trin ca th gii. Cng ngh x l hnh nh, cng ngh Th gic

my ngy mt c ng dng rng ri v l ti chnh cho nhiu nghin cu v

cng trnh khoa hc.

Vi cc nhn xt, nh gi trn, ng thi da vo kh nng v trnh bn thn,

phn sau nhm s trnh by Phng php thc hin ti c nhm khoanh

vng thc hin.

1.4. Phng php thc hin ti

Sau qu trnh tham kho cc bi bo nghin cu khoa hc, cc h thng, sn

phm pht hin t ng trn th trng, trong phm vi n tt nghip, nhm chng

em quyt nh thit k mt phn mm x l hnh nh c tc dng pht hin ngi t

ng bng mt camera. H thng pht hin t ng gm 4 phn chnh:

Thu thp video.

Theo di i tng.

Pht hin t ng.

a ra tn hiu cnh bo.

Phn mm c nhm la chn vit bng ngn ng C++ kt hp cng th vin

x l nh m ngun m OpenCV.

L do la chn phng php thc hin n:

Hin nay, xu hng s dng Th gic my tnh ang c pht trin v ng

dng rng ri vo nhiu lnh vc khc nhau, trong lnh vc Chm sc sc

khe con ngi ang c quan tm hng u.


32

Vic ch s dng mt camera xc nh t ng tuy cho kt qu khng chnh

xc bng vic s dng cm bin gn theo ngi nhng s c chi ph r hn,

tnh ng dng rng hn. Vic nghin cu phn mm c chnh xc tt

nht l tiu ch s mt ca nhm.

Vi vic la chn khoanh vng n nh vy, nhm c iu kin p dng

c cc kin thc c hc trn lp trong mn Lp trnh, Lp trnh nng

cao, X l nh s...vo thc t.

Vi vic s dng camera theo di s to iu kin thun li cho nhm tin

hnh thc nghim nhiu hn, t nng cao chnh xc ca phm mm.

Vic la chn ngn ng C++ gip cho tc x l nh cao hn khi c kt

hp vi cu trc phn cng ca Intel.

Vy trong n tt nghip, nhm chng em s tp trung thit k v lp trnh

mt phn mm x l nh chy trn my tnh c gn camera ngoi thu nhn tn hiu

Pht hin t ng. phn sau chng em s trnh by C s l thuyt, bao gm cc

nguyn l, k thut x l nh s c bn l nn tng ca vic thit k v lp trnh

phn mm sau ny.


33

Chng 2: C s l thuyt

2.1. Gii thiu v h thng x l nh

X l nh l mt lnh vc mang tnh khoa hc v cng ngh. N l mt ngnh

khoa hc mi m so vi nhiu ngnh khoa hc khc nhng tc pht trin ca n

rt nhanh, kch thch cc trung tm nghin cu, ng dng, c bit l my tnh

chuyn dng ring cho n.

X l nh tri qua cc bc nh sau: u tin, nh t nhin c thu nhn qua

cc thit b thu. Sau n c chuyn trc tip thnh nh s to thun li cho x

l tip theo. Hnh 2.1 di y m t cc bc c bn trong x lnh.

Hnh 2.1 Cc bc c bn trong x l nh

Thu nhn nh (Image Acquisition): nh c th nhn qua camera mu hoc

en trng. Thng nh nhn qua camera nh tng t (loi camera ng chun

CCIR vi tn s 1/25, mi nh 25 dng), cng c loi camera s ha l loi

photodiot to cng sng ti mi im nh. Camera thng dng l loi qut

dng, nh to ra c dng 2 chiu. Cht lng nh thu nhn c ph thuc vo

thit b thu, vo mi trng.

Tin x l (Image Processing): Sau b thu nhn, nh c th nhiu tng

phn thp nn cn a vo b tin x l nng cao cht lng. Chc nng chnh

ca b tin x l l lc nhiu, nng tng phn lm nh r hn, nt hn.

Phn vng nh (Segmentation): Phn vng nh l tch mt nh u vo thnh

cc vng thnh phn biu din phn tch, nhn dng nh. y l phn phc tp

nht trong x l nh v cng d gy li, lm mt chnh xc ca nh. Kt qu

nhn dng nh ph thuc rt nhiu vo cng on ny.

Thu nhn nh

Tin x l nh

Phn on nh

Biu din v m t

Nhn dng v ni suy

C S TRI THC


34

Biu din nh (Image Representation): u ra nh sau phn vng cha cc

im nh ca vng nh vi m lin kt vi cc vng ln cn. Vic bin i cc s

liu ny thnh dng thch hp l cn thit cho x l tip theo bng my tnh. Vic

chn cc tnh cht th hin nh gi l trch chn c trng gn vi vic tch cc

c tnh ca nh di dng cc thng tin nh lng hoc lm c s phn bit

lp i tng ny vi i tng khc trong phm vi nh nhn c.

Nhn dng v ni suy nh: Nhn dng nh l qu trnh xc nh nh. Qu

trnh ny thu c bng cch so snh vi mu chun c lu t trc. Ni suy

l phn on theo ngha da trn c s nhn dng. Theo l thuyt nhn dng, cc

m hnh ton hc v nh c phn theo 2 loi nhn dng c bn: nhn dng theo

tham s v nhn dng theo cu trc.

C s tri thc (Knowledge Base): nh l mt i tng kh phc tp v

ng nt, sng, s im nh, nhiu, Trong nhiu khu x l v phn tch nh

ngoi vic n gin ha cc phng php ton hc m bo tin li cho x l,

ngi ta mong mun bt chc quy trnh tip nhn v x l nh theo cch ca con

ngi. Trong cc bc x l , nhiu khu hin nay x l theo cc phng

php tr tu con ngi. V vy y cc c s tri thc c pht huy.

Biu din nh: nh sau khi s ha s c lu vo b nh hoc chuyn sang

cc khu tip theo phn tch. Nu lu tr nh trc tip t cc nh th i hi

dung lng b nh cc ln. Thng thng, cc nh th c c t li theo cc

c im ca nh c gi l cc c trng nh nh: bin nh, vng nh. Mt s

phng php biu din thng dng l: biu din bng m chy, biu din bng m

xch v biu din bng m t phn.


35

Hnh 2.2 S phn tch v x l nh, v lu thng tin gia cc khi

2.2. Thu nhn nh

2.2.1. Cc thit b thu nhn nh

Hai thnh phn cho cng on ny l linh kin nhy vi ph nng lng in

t trng, loi th nht to tn hiu in u ra t l vi mc nng lng m b

cm bin (i din l camera); loi th hai l b s ho.

a) B cm bin nh

My chp nh, camera c th ghi li hnh nh. C nhiu loi my cm bin

(Sensor) lm vic vi nh sng nhn thy v hng ngoi nh: Micro Densitometers,

Image Dissector, Camera Divicon, linh kin quang in bng bn dn. Cc loi

cm bin bng chp nh phi s ho l phim m bn hoc chp nh. Camera

divicon v linh kin bn dn quang in c th cho nh ghi trn bng t c th s

ho. Trong Micro Densitometer phim v nh chp c gn trn mt phng hoc

cun quang trng. Vic qut nh thng qua tia sng (v d tia Laser) trn nh ng

thi dch chuyn mt phim hoc quang trng tng i theo tia sng. Trng hp

dng phim, tia sng i qua phim.

By gi chng ta cp n tt c cc khi trong h thng.

Thit b nhn nh: Chc nng ca thit b ny l s ha mt bng tn s c

bn ca tn hiu truyn hnh cung cp t mt camera, hoc t mt u my

VCR. nh s sau c lu tr trong b m chnh. B m ny c kh

nng c a ch ha (nh mt PC) n tng im bng phn mm.


36

Thng thng thit b ny c nhiu chng trnh con iu khin c th

lp trnh c thng qua ngn ng C.

Camera: Tng qut c hai kiu camera: kiu camera dng n chn khng

v kiu camera ch dng bn dn. c bit l trong lnh vc ny, camera

bn dn thng hay c dng hn. Camera bn dn cng c gi l

CCD camera do dng cc thanh ghi dch c bit gi l thit b gp

(Charge-Coupled Devices- CCDs). Cc CCD ny chuyn cc tn hiu nh

sang t b cm nhn nh sng b tr pha trc camera thnh cc tn

hiu in m sau c m ha thnh tn hiu TV. Loi camera cht

lng cao cho tn hiu t nhiu v c nhy cao vi nh sng. Khi chn

camera cn ch n cc thu knh t 18 n 108 mm.

Mn hnh video: Nn s dng loi mn hnh cht lng cao, v mn hnh

cht lng thp c th lm bn nhm ln kt qu. Mt mn hnh 9 inch l

cho yu cu lm vic. hin th nh mu, nn dng mt mn hnh a

h.

My tnh: Cn c mt my tnh P4 hoc cu hnh cao hn. chc chn,

cc my ny phi c sn cc khe cm cho phn x l nh. Cc chng

trnh thit k v lc nh c th chy trn bt k h thng no. Cc chng

trnh con hin th nh dng vi mch VGA v c sn trn a km theo. Cc

chng trnh con hin th nh cng h tr cho hu ht cc vi mch SVGA.

b) H ta mu

T chc quc t v chun ha mu CIE (Commission Internationale

dEclairage) a ra mt s chun biu din mu. Cc h ny c cc chun ring.

H chun mu CIE-RGB dng 3 mu c bn R, G, B v k hiu RGBCIE phn

bit vi cc chun khc. Ngi ta dng h ta 3 mu R-G-B (tng ng vi h

ta x-y-z) biu din mu nh sau:


37

Hnh 2.3 H ta RGB

Trong cch biu din ny ta c: + lc + l = 1

Cng thc trn c gi l cng thc Maxwell.

H ta mu do CIE xut c tc dng nh mt h quy chiu v khng biu

din ht cc mu. Trn thc t, ph thuc vo cc ng dng khc nhau ngi ta

a ra cc h biu din mu khc nhau. Th d:

- H NTSC: dng 3 mu R, G, B p dng cho mn hnh mu, k hiu

RGBNTSC;

- H CMY (Cyan Magenta Yellow): thng dng cho in nh mu;

- H YIQ: cho truyn hnh mu.

Vic chuyn i gia cc khng gian biu din mu c thc hin theo

nguyn tc sau:

Nu gi X l khng gian biu din cc mu ban u, X l khng gian biu

din mu mi. A l ma trn biu din php bin i. Ta c quan h sau: X = AX

V d, bin i h ta mu RGBCIE sang h ta mu RGBNTSC ta c cc

vc t tng ng :

Px =

v Px =

2.2.2. Ly mu v lng t ha

nh sau khi ly t camera cn chuyn sang dng thch hp x l bng my

tnh. Phng php bin i mt nh lin tc trong khng gian cng nh theo gi tr

thnh sng s ri rc c gi l s ha nh. Vic bin i ny gm 2 bc :


38

o gi tr trn cc khong khng gian gi l ly mu.

nh x cng o c thnh mt s hu hn cc mc ri rc c

gi l lng t ha.

a) Ly mu

Ly mu l mt qu trnh, qua nh c to nn trn mt vng c tnh lin

tc c chuyn thnh cc gi tr ri rc theo ta nguyn. Gm 2 la chn:

Mt l: khong ly mu.

Hai l: cch th hin dng mu.

La chn th nht c m bo nh l thuyt ly mu ca Shannon. La

chn th hai lin quan n o (Metric) c dng trong min ri rc.

nh l ly mu ca Shannon : Gi s g(x) l mt hm gii hn gii l

bin i Fourier ca n l G(x) = 0 i vi cc gi tr x> Wx. Khi

g(x) c th c khi phc li t cc mu c to ti cc khong

u n. Tc l

Cc dng ly mu (Tesselation) : Dng ly mu (Tesselation) im nh l cch

bi tr cc im mu trong khng gian hai chiu. Mt s dng mu im nh c

cho l dng ch nht, tam gic, lc gic.

Hnh 2.4 Cc dng mu im nh.

(a) Mu im nh ch nht. (b) Mu im nh tam gic. (c) Mu im nh

lcgic

b) Lng t ha

Lng t ho l nh x t cc s thc m t gi tr ly mu thnh mt gii hu

hn cc s thc. Ni cch khc, l qu trnh s ho bin .


39

2.2.3. Mt s phng php biu din nh

Sau bc s ha, nh s c lu tr hay chuyn sang giai on phn tch.

Trc khi cp n vn lu tr nh, cn xem xt nh s c biu din ra sao

trong b nh my tnh. Di y gii thiu mt sphng php biu din thng

dng:

- Biu din m lot di (Run-length Code)

- Biu din m xch (Chain Code)

- Biu din m t phn (Quad Tree Code)

a) M lot di

Phng php ny hay dng biu din cho vng nh hay nh nh phn. Mt

vng nh R c th biu din n gin nh mt ma trn nh phn:

U(m,n) = 1 ( ,)

0

Vi cc biu din trn, mt vng nh hay nh nh phn c xem nh chui 0

hay 1 an xen. Cc chui ny c gi l mch (run). Theo phng php ny, mi

mch s c biu din bi a ch bt u ca mch v chiu di mch theo dng

{, chiu di}.

b) M xch

M xch thng c dng biu din bin ca nh. Thay v lu tr ton b

nh, ngi ta lu tr dy cc im nh nh A, BM. Theo phng php ny, 8

hng ca vect ni 2 im bin lin tc c m ha. Khi nh c biu din

qua im nh bt u A cng vi chui cc t m. iu ny c minh ha trong

hnh di y:

Hnh 2.5 Hng cc im bin v m tng ng : A11070110764545432

c) M t phn

Theo phng php m t phn, mt vng nh coi nh bao kn mt hnh ch


40

nht. Vng ny c chia lm 4 vng con (Quadrant). Nu mt vng con gm ton

im en (1) hay ton im trng (0) th khng cn chia tip. Trong trng hp

ngc li, vng con gm c im en v trng gi l vng khng ng nht, ta tip

tc chia thnh 4 vng con tip v kim tra tnh ng nht ca cc vng con . Qu

trnh chia dng li khi mi vng con ch cha thun nht im en hoc im

trng. Qu trnh to thnh mt cy chia theo bn phn gi l cy t phn. Nh

vy, cy biu din nh gm mt chui cc k hiu b (black), w (white)v g (grey)

km theo k hiu m ha 4 vng con. Biu din theo phng php ny u vit hn

so vi cc phng php trn, nht l so vi m lot di. Tuy nhin, tnh ton s

o cc hnh nh chu vi, m men l tng i kh khn.

2.2.4. Cc nh dng nh c bn

nh thu c sau qu trnh s ha thng c lu li cho cc qu trnh x l

tip theo hay truyn i. Trong qu trnh pht trin ca k thut x l nh, tn ti

nhiu nh dng nh khc nhau t nh en trng (vi nh dng IMG), nh a cp

xm cho n nh mu: (BMP, GIF, JPEG). Tuy cc nh dng ny khc nhau,

song chng u tun theo mt cu trc chung nht. Nhn chung, mt tp nh bt k

thng bao gm 3 phn:

Mo u tp (Header): l phn cha cc thng tin v kiu nh, kch

thc, phn gii, s bit dng cho 1 pixel, cch m ha, v tr bng

mu

D liu nn (Data Compression): S liu nh c m ha bi kiu m

ha ch ra trong phn Header.

Bng mu (Palette Color): Bng mu khng nht thit phi c v d khi

nh l en trng. Nu c, bng mu cho bit s mu dng trong nh v

bng mu c s dng hin th mu ca nh.

2.3. X l nng cao cht lng nh

Nng cao cht lng nh l lm cho nh c tng cng ph hp hn nh ban

u cho mt ng dng c th no . y l bc cn thit trong x l nh, gm 2

cng on khc nhau: tng cng nh v khi phc nh. Cc ton t c s dng


41

trong k thut nng cao cht lng nh bao gm:

Ton t im: nh x gi tr mc xm u vo.

Ton t khng gian: lc khng gian.

Ton t bin i: thc hin trong min bin i

2.3.1. Ci thin nh s dng cc ton t im

a) Tng tng phn (Contrast Stretching)

tng phn biu din s thay i sng ca i tng so vi nn, hay ni

cch khc tng phn l ni ca im nh hay vng nh so vi nn.

Vi nh c tng phn thp, iu chnh li tng phn ca n ta cn

iu chnh li bin trn ton di hay trn di c gii hn bng cch bin i

tuyn tnh bin u vo hay phi tuyn. Khi dng hm tuyn tnh cc dc , ,

phi chn ln hn mt trong min cn dn. Cc tham s a v b (cc cn) c th

chn khi xem xt lc xm ca nh.

Hnh 2.6 Dn tng phn

Vi u l nh mc xm u vo, v l nh mc xm u ra, ta c:

v =

,0 < ( )+ , <

( )+ , <

- Tch nhiu v phn ngng

Tch nhiu l trng hp c bit ca dn tng phn khi h s gc = =0.

Tch nhiu c ng dng c hiu qu gim nhiu khi bit tn hiu vo trn

khong [a, b].

Phn ngng l trng hp c bit ca tch nhiu khi a=b=const. Trong

trng hp ny, nh u vo l nh nh phn (c 2 mc). Phn ngng thng dng


42

trong k thut in nh 2 mu v nh gn nh phn khng cho nh nh phn khi qut

nh do c nhiu t b cm bin v bin i ca nn v d trng hp lc nhiu ca

nh vn tay.

Hnh 2.7. Tch nhiu v phn ngng

b) Bin i m bn (Image Negative)

m bn nhn c bng php bin i m. Php bin i rt c nhiu hu ch

trong cc phim nh dng trong cc nh y hc.

v = (2n 1) u vi 0 u, v(2n 1)

Hnh 2.8. Bin i m bn.

(a) nh ban u. (b) nh bin i m bn

c) Trch chn bt (Bit Extraction)

Mi im nh thng c m ha trn B bit (2n). trch chn bit v hin th

chng, ta dng bin i sau:


43

vt = 2 1 nu = 1

0 = 0

d) Nn di (range compression)

Di ng ca nh c bin i n v rt ln nn i khi ch thy mt s t cc

pixel. V vy, cn thu nh di ng ca nh li thun tin cho vic quan st.

Ngi ta dng php bin i sau: V = c.log10(1+|u|)

e) Tr nh (Image subtraction)

Tr nh dng so snh 2 nh vi nhau, thng l nh ca cng 1 i tng

nhng ti 2 thi im khc nhau. Tr nh c dng tch nhiu khi nn.

Phng php tr nh: tr theo tng bt

V(i, j) = a. |u1(i,j) u2(i,j)| + b vi a,b l hng s

ng dng ca tr nh: dng trong d bo thi tit, trong y hc.

f) Cn bng mc xm (Histogram Equalization)

Mc xm biu din tn s xut hin tng i ca cc mc xm c trong

nh.

Coi gi tr im nh u 0 l mt bin ngu nhin chm phn b mt xc

sut l pu(u) v hm phnb tch lu l Fu()=P[u ].

Bin ngu nhin v c nh ngha di y s phn b u trong khong [0,1].

v = Fu(u) = ()

Thc hin trn nh s:

nh vo u c mc xm h(xi)

Pu(xi) = h(xi)| (xi) ; i = 0, 1, , L-1

nh ra v* cng gi thit l c L mc c cho bi v u(xi)

v* Int[(v-vmin)(L-1)/(1-vmin)+0,5]

Hnh 2.9. Thc hin gn ng cn bng mc xm

Cn bng mc xm c th dn ti bo ha mt s vng trong nh, lm mt

cc chi tit v cc thng tin tn s cao c th cn thit cho vic c, bin dch nh.


44

2.3.2. Ci thin nh dng ton t khng gian (Spatial Operators)

Ci thin nh l lm cho nh c cht lng tt hn theo s dng. Thng

l nh thu nhn c nhiu cn phi loi b nhiu hay nh khng sc nt b m hoc

cn lm r cc chi tit nh ng bin nh. Cc ton t khng gian dng trong k

thut tng cng nh c phn nhm theo cng dng: lm trn nhiu, ni bin.

lm trn nhiu hay tch nhiu, ngi ta s dng cc b lc tuyn tnh (lc trung

bnh, thng thp) hay lc phi tuyn (trung v, gi trung v, lc ng hnh). lm

ni cnh (ng vi tn s cao), ngi ta dng cc b lc thng cao, lc Laplace.

a) Lm trn nhiu (Smoothing) bng lc tuyn tnh

Cc b lc lm trn c s dng lm m v lm gim nhiu. Lm m

c s dng cc bc tin x l nh loi b cc chi tit nh khi nh trc khi

thc hin trch, chn i tng (ln); xa cc cch qung nh trn cc ng thng

hoc ng cong. Vi cc loi nhiu khc nhau th cn c b lc thch hp.

Lc trung bnh khng gian (Spatial Averaging)

Vi lc trung bnh, mi im nh c thay th bng trung bnh trng s ca

cc im ln cn v c nh ngha nh sau:

v(m,n) = (,)( , ),

W: ca s/mt n lc c chn hp l

a(k,l): h s lc ; v(m,n) l nh u ra; y(m,n) l nh u vo

Gi thit nh ban u y(m,n)=u(m,n)+(m,n) vi (m,n) l nhiu trng k

vng=0 v phng sai= 2. nh sau khi lc:

v(m,n) =

( , )+ ( ,),

(m,n) c k vng=0 v phng sai= 2/NW

Nh vy, cng sut nhiu gim i NW ln.

Mt s mt n H dng trong lc trung bnh

H1 =

1 1 11 1 11 1 1

H2 =

1 1 11 2 11 1 1

H3 =

1 2 12 4 21 2 1

Cc mt n dng trong cc trng hp khc nhau ca lc trung bnh c trng

s. Lc trung bnh c trng s chnh l thc hin chp nh u vo vi nhn chp H.

Lc thng thp


45

Lc thng thp thng c s dng lm trn nhiu.V nguyn l ca b lc

thng thp ging nh trnh by trn. Trong k thut ny ngi ta hay dng mt

s nhn chp c dng sau:

Ht1 =

0 1 01 2 10 1 0

Hb =

()1 1 1 1

Vi (m,n) l nhiu cng c phng sai 2n. Theo cch tnh ca lc trung bnh

ta c:

Y[m,n] =

( , )+ [ ,],

Hay: Y[m,n] =

( , )+

,

Nh vy, nhiu gim i NW ln.

Lc ng hnh (Homomophic Filtering): L k thut tng cng cht lng

nh khi nh b nh hng bi nhiu c bn cht nhn. Vi lc ng hnh, c th

gim di ng, tng tng phn cc b.

M hnh to nh chiu phn x: u(m,n) = i(m,n).r(m,n)

Vi i(m,n) l chiu sng, ng gp chnh vo di ng v thay i chm.

r(m,n): phn x, th hin chi tit ca i tng, ng gp ch yu vo

tng phn ni b v c gi thit l bin i rt nhanh.

Thc hin lc ng hnh ta c: log[u(m,n)] = log[i(m,n)] + log[r(m,n)]

Suy hao log[i(m,n)] lm gim di ng

Tng log[r(m,n)] tng phn gii ni b.

b) Lm trn nhiu bng lc phi tuyn

Cc b lc phi tuyn cng hay c dng trong k thut tng cng nh. Trong

k thut ny, ngi ta dng b lc trung v, gi trung v, lc ngoi. Vi lc trung v,

im nh u vo s c thay th bi trung v cc im nh cn lc gi trung v s

dng trung bnh cng ca 2 gi tr trung v (trung bnh cng ca max v min).

Lc trung v (Median Filtering)

Gi tr pixel c thay th bi pixel trung tm ca dy cc pixel ln cn

v(m,n)=median{u(m-k,n-l),(k,l) W}

Kthut ny i hi gi tr cc im nh trong ca s phi xp theo th t tng

hay gim dn so vi gi tr trung v. Kch thc ca s thng c chn sao cho


46

s im nh trong ca s l l. Cc ca s hay dng l ca s c kch thc 3x3,

hay 5x5 hay 7x7. Lc trung v c cc tnh cht sau:

- Lc trung v l loi lc phi tuyn:

median{u(m) + v(m)} median{u(m)} + median{v(m)}

- C li cho vic loi b cc im nh hay cc hng m vn bo tan phn

gii.

- Hiu qu gim khi s im trong ca s ln hay bng mt na s im

trong ca s.

c) Mt n g sai phn v lm nhn

Mt n g sai phn dng kh ph bin trong cng ngh in nh lm p nh.

Vi k thut ny, tn hiu u ra thu c bng tn hiu ra ca b lc gradient hay

lc di cao b sung thm u vo: v(m,n)=u(m,n) + g(m,n)

Vi >0, g(m,n) l gradient ti im (m,n). Hm gradient dng l hm Laplace

G(m,n)=u(m,n) {u(m-1,n) + u(m+1,n) + u(m,n+1)}/2

y chnh l mt n ch thp.

d) Lc thng thp, thng cao v thng di

Nu hLP(m, n)biu din blc thng thp FIR (Finite Impulse Response) th b

lc thng cao hHP(m, n) c th c nh ngha:

hHP(m, n) = (m, n) - hLP(m, n)

B lc di thng c th nh ngha nh sau:

HHP(m, n)= hL1(m, n) hL2(m, n)

Vi hL1 v hL2 l cc b lc thng thp.

B lc thng thp thng dng lm trn nhiu v ni suy nh. B lc thng cao

dng nhiu trong trch chn bin v lm trn nh, cn b lc di thng c hiu qu

lm ni cnh.

e) Khuch i v ni suy nh

C nhiu ng dng cn thit phi phng i mt vng ca nh. C ngha l ly

mt vng ca nh cho v cho hin ln nh mt nh ln. C 2 phng php c

dng l lp (Replication) v ni suy tuyn tnh (Linear Interpolation).

Phng php lp


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Ngi ta ly mt vng ca nh kch thc MxNv qut theo hng. Mi im

nh nm trn ng qut s c lp li 1 ln v hng qut cng c lp li 1 ln

na. Nhvy, ta thu c nh vi kch thc 2Nx2N. iu ny tng ng vi

vic chn thm mt hng 0 v 1 ct 0 ri chp vi mt n H. Mt n H:

H = 1 11 1

Kt qu thu c: v(m,n) = u(k,l) vi k = m/2 v l = n/2

Phng php ni suy tuyn tnh

Gi s c mt ma trn im nh. Theo phng php ni suy tuyn tnh, trc

tin, hng c t vo gia cc im nh theo hng. Tip sau, mi im nh dc

theo ct c ni suy theo ng thng. Ta dng mt n:

H =

1/4 1/4 1/41/2 1 1/21/4 1/2 1/4

Ni suy vi bc cao hn cng c th p dng cch trn. Th d, ni suy vi bc

p (p nguyn), ta chn p hng vi cc s 0, ri p ct vi cc s 0. Cui cng, tin

hnh nhn chp p ln nh vi mt n H trn.

2.3.3. Cc php ton hnh thi hc

Hnh thi l thut ng ch s nghin cu v cu trc hay hnh hc topo ca i

tng trong nh. Phn ln cc php ton ca Hnh thi c nh ngha t hai

php ton c bn l php m rng (Dilation) v php n mn (Erosion).

Cc php ton ny c nh ngha nh sau: gi thit ta c i tng X v phn

t cu trc (mu) B trong khng gian Euclide hai chiu. K hiu Bx l dch chuyn

ca B ti v tr x.

nh ngha Dilation: Php m rng ca X theo mu B l hp nht tt c cc

Bx vi x thuc X. Ta c: X B = x


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Hnh 2.10 nh sau Dilation.

(a) nh gc. (b) nh s dng dilation

Nhn xt: Trong nh, nu mc xm ( sng) cng ln tc l cha cc gi tr

dng th sau khi qua php dilation th nh s cng sng hn v cc phn ti s cng

b thu nh hoc mt hn i.

nh ngha Erosion: Php n mn ca X theo B l tp hp tt c cc im x

sao cho Bx nm trong X. Ta c: X B = {x:BxX}

Hnh 2.11 nh sau php erosion.

(a) nh ban u. (b) nh sau erosion

Nhn xt: Sau khi n mn nh, kt qu s cho mt bc nh ti hn v nhng

phn sng nh hoc t s b thu nh hoc loi b hon ton. Nu kch thc ca

nhng vng sng nh hn kch thc ca nhn to nh th nhng vng ny sau php

co nh s b bin mt.

nh ngha Open: Php ton m (Open) ca X theo cu trc B l tp hp cc

im nh X sau khi co v gin n lin tip theo B. Ta c:


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OPEN(X,B) = (X B) B

nh ngha Close: Php ton ng (Close) ca X theo cu trc B l tp hp cc

im ca nh X sau khi gin n v co lin tip theo B. Ta c:

CLOSE(X,B) = (X B) B

Hnh 2.12 nh sau open v close.

(a) nh gc. (b) nh sau php open. (c) nh sau php close

Nhn xt: bc nh sau open th nhng vng no sng mt cch khc thng

so vi nhng vng xung quanh s c h mc xm xung gn vi nhng vng

khc. bc nh sau close nhng vng no ti (c mc xm thp) mt cch khc

thng so vi nhng vng xung quanh th s c nng mc xm ln gn bng

nhng vng khc.

2.3.4. Khi phc nh

Khi phc nh l phc hi li nh gc so vi nh ghi c b bin dng. Ni

cch khc, khi phc nh l cc k thut ci thin cht lng nhng nh ghi m

bo gn c nh nh tht khi nh b mo.

K thut khi phc nh c th c xc nh nh vic c lng li nh gc

hay nh l tng t nh quan st c bng cch o ngc li nhng hin tng

gy bin dng, qua nh c chp. Nh vy, k thut khi phc nh i hi kin

thc v cc hin tng gy bin dng nh.

Cc k thut khi phc nh:

- M hnh khi phc nh c: m hnh to nh, m hnh gy nhiu, m hnh

quan st.

- Lc tuyn tnh c: lc ngc, p ng xung, lc hu hn FIR.

- Cc k thut khc: Entropy cc i, m hnh Bayes, gii chp.


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a) Cc m hnh quan st v to nh

M hnh quan st

u tin, cn xem xt nh c hnh thnh nh th no; sau bin i ngc

(thc hin lc ngc) kh nhiu thu li nh ban u.

T phng trnh bin i tn hiu nh c nhiu, chng ta c th vit:

V(m,n) = g[w(m,n)] + (m,n)

Vi w(m,n) l u ra ca h thng tuyn tnh vi p ng hai chiu h(m,n) ta

c:

w(m,n)= ( ,). ( , )

Nhiu (m,n) c th gm hai thnh phn: nhiu tch 1(m,n), nhiu cng

2(m,n)

(m,n)=f[g(w(m,n))].1(m,n) + 2(m,n)

Trong cc hm g(.) v f(.) l cc bin i c trng cho qu trnh pht hin

v lu tr nh.

Hnh 2.13 Qu trnh pht hin v lu tr nh

M hnh nhiu

M hnh nhiu l m hnh tng qut. Trong h thng c th nh quang in, m

hnh nhiu gy bin dng c biu din c th nh sau:

(m,n)= ( ,). 1(m,n) + 2(m,n)

Trong 1(m,n) l nhiu ph thuc thit b. 2(m,n) biu din nhiu gy ra do

nhit v thng c m hnh ha theo nhiu trng.

Cc thnh phn b nhiu 1(m,n) tc ng gy kh khn cho vic khi phc nh.

gii quyt theo phng php gn ng ngi ta dng gi tr trung bnh khng

gian w thay cho w, tc l: w=w(m,n)

Khi : (m,n)=f[g(w)].1(m,n) + 2(m,n) v (m,n) tr thnh m hnh nhiu

trng Gauss.


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b) Cc b lc

K thut lc ngc (Inverse Filter)

Hnh 2.14 K thut lc ngc

gT(x) = g-1[g(x)] vi g-1(x) = x

hT(x,y,k,l) = h-1(x,y,k,l)

FT[ (,,)(,; ,)] = ( , )

Vic thit k b lc ngc kh kh khn, do vy chuyn sang bin i Fourier 2

v, do :

HT(w1,w2) = 1/H(w1,w2))

Trong bin i ngc Fourier ca H(w1,w2) l h(x,y)

Nh vy tm c HT(w1,w2) , tng t cng xc nh c GT(w1,w2) .

2.4. Phng php pht hin bin

2.4.1. K thut pht hin bin

ng bin trong nh thng c nh ngha mt cch c bn bi s thay i

gi tr mc xm ca cc pixel trong vng ln cn. C 2 phng php pht hin bin

c bn:

Pht hin bin trc tip: Phng php ny lm ni bin da vo s bin

thin mc xm ca nh. Nu ly o hm bc nht ca nh: ta c phng

php Gradient. Nu ly o hm bc hai ca nh: ta c phng php

Laplace. Hai phng php ny c gi chung l phng php d bin

cc b.

Pht hin bin gin tip: Nu bng cch no ta phn c nh thnh

cc vng th ranh gii gia cc vng gi l bin. K thut d bin v

phn vng nh l hai bi ton i ngu nhau.

Quy trnh pht hin bin:

Bc 1: Do nh ghi c thng c nhiu, bc mt l phi lc nhiu.


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Bc 2: Lm ni bin s dng cc ton t pht hin bin.

Bc 3: nh v bin. Ch rng k thut ni bin gy tc dng ph l

gy nhiu lm mt s bin gi xut hin do vy cn loi b bin gi.

Bc 4: Lin kt v trch chn bin.

2.4.2. Phng php pht hin bin cc b

a) Ton t gradien (Gradient operator)

L ton t vi phn bc nht, tnh gradien (trng c hng) theo mt hng no

. Thng tin gradien thu c sau c s dng tng cng hay trch c

im (feature extraction) phc v cho mc ch phn vng nh (image

segmentation).

Hnh 2.15 Dng phn b (profile) sng v vi phn bc nht (gradien) ca

ng vin 1 chiu thng thng.

Gradien ca nh I(x,y) c tnh bi : I(x,y) = (,)

+

(,)

Gradien ca nh ri rc I(m,n) c tnh:

I(m,n) = mag[I(m,n)] = [2x(m,n) + 2y(m,n)]1/2 = |x(m,n)| + |y(m,n)|

Vi x v y tng ng l gradien theo phng x v phng y tng ng.

x(m,n) = I(m,n+1) I(m,n)

y(m,n) = I(m+1,n) I(m,n-1)

Cc bc thc hin pht hin ng bin s dng ton t gradient


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Hnh 2.16 M hnh pht hin ng bin dng ton t Gradient

Xt mt s ton t gradient tiu biu nh ton t sobel, prewitt, la bn.

Ton t Sobel

Mt n ton t Sobel s dng 2 mt n theo phng x, y nh sau:

Hx = 1 0 1 2 0 2 1 0 1

Hy = 1 2 10 0 01 2 1

Ikq = I Hx + I Hy

Ton t Prewitt

Mt n ton t Sobel s dng 2 mt n theo phng x, y nh sau:

Hx = 1 0 1 1 0 1 1 0 1

Hy = 1 1 10 0 01 1 1

Ikq = I Hx + I Hy

Ton t la bn

K thut s dng 8 mt n nhn chp theo 8 hng 00, 450, 900, 1350, 1800,

2250, 2700, 3150.

HB = 1 1 10 0 01 1 1

HTB = 1 1 01 0 10 1 1

HT = 1 0 11 0 11 0 1

HTN = 0 1 11 0 11 1 0

HN = 1 1 10 0 01 1 1

HN = 1 1 01 0 10 1 1

H = 1 0 1 1 0 1 1 0 1

HB = 0 1 11 0 11 1 0

Cc bc tnh ton thut ton la bn :

Bc 1 : tnh I Hi ; i = 1,8.

Bc 2: I Hi

Nhn xt:

Ton t Prewitt c th tch sn ng tt hn ton t Sobel, trong khi

ton t Sobel tch cc sn trn cc im ng cho tt hn. Ton


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t la bn nhy vi nhiu. Cc ton t Gradient v Sobel gim nhiu do

tc dng ca lc trung bnh cc im ln cn.

Cc mt n ca cc ton t trn c kch thc 2x2 hoc 3x3 chiu. Cc

mt n c s chiu ln hn cng c s dng.

Cc ton t k trn u s dng cc mt n theo hai chiu (x,y) tc l

bn hng (-x, y; -y, y). Vi mc ch cho kt qu tinh v chnh xc hn

(khi m tc v b nh my tnh tt).

Nhc im ca cc k thut Gradient l nhy cm vi nhiu v to cc bin

kp lm cht lng bin thu c khng cao. khc phc hn ch v nhc im

ca phng php Gradient, trong s dng o hm ring bc nht ngi ta ngh

n vic s dng o hm ring bc hai hay ton t Laplace.

b) Ton t Laplace

Hnh 2.17 Profile sng, vi phn bc nht v bc hai (Laplace) ca ng

vin 1 chiu thng thng

Laplace ca nh I(x,y): 2I(x,y) = Ixx(x,y) + Iyy(x,y)

Vi Ixx, Iyy tng ng l cc vi phn bc hai theo phng x v phng y.

Mt s mt n dng trong ton t Laplace

H1 = 0 1 01 4 10 1 0

H2 = 1 1 11 8 11 1 1

H3 = 1 2 12 4 21 2 1

Tm ng bin:


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L tm cc im vt khng ca 2I(x,y).

Tuy nhin, ton t Laplace to ra nhiu ng bin sai, thng l ti cc

vng c phng sai cc b l nh.

Hnh 2.18 M hnh pht hin ng bin dng ton t Laplace

Tm li, k thut theo ton t Laplace to ng bin mnh (c rng 1

pixel). Nhc im ca k thut ny rt nhy vi nhiu, do vy ng bin thu

c thng km n nh.

2.4.3. Pht hin bin gin tip

Chu tuyn ca mt i tng nh : l dy cc im ca i tng nh P1,,Pn

sao cho Pi v Pi+1 l cc 8-lng ging ca nhau (i=1,,n-1) v P1 l 8-lng ging

ca Pn. K hiu . Ta xt k thut d bin i tng nh theo chu tuyn.

Thut ton d bin tng qut

Biu din i tng nh theo chu tuyn thng da trn cc k thut d bin.

C hai k thut d bin c bn. K thut th nht xt nh bin thu c t nh vng

sau mt ln duyt nh mt th, sau p dng cc thut ton duyt cnh th.

K thut th hai da trn nh vng, kt hp ng thi qu trnh d bin v tch

bin. y ta quan tm cch tip cn th hai.

Trc ht, gi s nh c xt ch bao gm mt vng nh 8-lin thng , c

bao bc bi mt vnh ai cc im nn. D thy l mt vng 4-lin thng ch l

mt trng ring ca trng hp trn.

V c bn, cc thut ton d bin trn mt vng u bao gm cc bc sau:

Xc nh cp nn-vng xut pht

Xc nh cp nn-vng tip theo

La chn im bin vng


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Nu gp li cp xut pht th dng, nu khng quay li bc 2.

2.5. Phn vng nh

Phn vng nh l bc then cht trong x l nh. Giai on ny nhm phn tch

nh thnh nhng thnh phn c cng tnh cht no da theo bin hay cc vng

lin thng.

Hnh 2.19 Phn vng nh.

(a) nh ban u. (b) nh c phn vng

C 3 phng php phn vng nh chnh

Phn vng da vo im nh hay phn vng da vo ly ngng (pixel-

based or thresholding method).

Phn vng da vo ng bin (edge-based method).

Phn vng da theo min/vng (region-based method).

2.5.1. Phn vng nh da vo ly ngng

c im

y l phng php da trn cc thng k mc xm ca nh to ra

cc vng ng thuc v cc i tng c trong nh.

Phng php phn vng n gin nht , tnh ton nhanh, c th thc hin

d dng trong thi gian thc s dng phn cng chuyn bit.

Da trn phn tch mc xm xc nh mt hay nhiu mc ngng

xp sp tng pixel trong nh.

Vic chn ngng rt quan trng. N bao gm cc bc :


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Xem xt lc xm ca nh xc nh cc nh v cc khe. Nu nh

c dng rn ln (nhiu nh v khe), cc khe c th dng chn

ngng.

Chn ngng T sao cho mt phn xc nh trc ca ton b s mu l

thp hn T.

iu chnh ngng da trn lc xm ca cc im ln cn.

Chn ngng theo lc xm ca nhng im tha mn tiu chun

chn.

Khi c m hnh phn lp xc sut, vic xc nh ngng da vo tiu

chun xc sut nhm cc tiu xc sut sai shoc da vo mt stnh

cht khc ca lut Bayes.

La chn mc ngng theo cng thc:

g(x,y) = 1,(,)>

0,(,)

2.5.2. Phn vng da vo ng bin

Phn vng theo ng bin da trn cc thng tin v ng bin ca nh xc

nh cc ng bao ca cc i tng. Cc ng bao ny sau c phn tch,

sa i nu cn thit nhm to ra cc vng ng thuc v cc i tng c trong

nh.

Hnh 2.20 Cc bc phn vng da vo ng bin

a) Pht hin ng bin

S dng cc ton t Gradient (Sobel, Prewitt, la bn) hay Laplace (Laplace

thng, LoG) xc nh ln v hng ca ng bin ti tng pixel.

b) Bm theo ng bin

Nhm ghp, ni cc ng bin thnh cc vng kn, t tm kim theo tng

pixel xt s lin kt gia cc on ng bin. C th s dng tiu chun ging


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nhau gia cc pixel ng bin hay dng cc tnh cht hay xp x hnh hc tng

cng i vi cc pixel b nh hng bi nhiu, artifact hay li hnh hc.

c) nh du vng

Cc vng gii hn bi ng bin kn tm c bc bm theo ng bin

cn phi c nh du xc nh (region filling, labeling, coloring)

Cc phng php: kim tra chn l (parity check), m xch (chain code), to

nhn (seeding).

X l sau

Loi b cc vng nh.

Gp cc vng ging nhau gn nhau.

Thut ton

Tm 1 vng nh Rs.

Tm cc vng ln cn vi Rs.

Vng ln cn Ra no c mc xm trung bnh gn vi Rs nht s c gp

vi Rs.

Lp li bc 1-3 n khi kch thc cc vng ln hn 1 gi tr no .

2.5.3. Phn vng da theo min/vng

Gm 2 phng php chnh: pht trin vng (region growing) v chia vng

(region splitting):

Phng php pht trin vng: cc pixel ln cn nhau c gp li

vi nhau to thnh cc vng ng theo mt tiu chun ging nhau

nh trc.

Phng php chia vng: ton b nh hay cc vng ln c chia

thnh 2 hay nhiu vng nh theo tiu chun khc nhau.

S khc nhau gia phn vng theo ngng v theo vng/min l: phn vng

da theo vng/min s cho cc vng gm cc pixel lin kt, phn vng da theo

ngng c th to ra cc vng trng v pixel khng lin kt.

a) Chia vng

Phng php chia vng c tin hnh qua cc bc sau:


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Chia ton b nh hay cc vng i tng ln thnh cc vng nh.

Kim tra tnh ng nht ca tng vng nh. Nu khng tha mn th

tip tc chia.

Gp cc vng ln cn ging nhau thnh 1 vng ln.

b) Pht trin vng

Phng php ny gp cc pixel ln cn vo vng cho n khi khng cn pixel

ln cn no iu kin ghp vp vng.

Yu cu 2 tiu chun:

Tiu chun ging nhau quyt nh vic ghp pixel vo vng.

Tiu chun kt thc quyt nh kt thc qu trnh ghp pixel vo

vng. Thng da trn s lng nh nht hay phn trm cc pixel

ln cn i hi tha mn tiu chun ging nhau cho vic ghp

pixel vo vng.

Pht trin vng lin kt trng tm (Centroid Linkage Region Growing)

Cc bc tin hnh:

Qut tng pixel X0 theo kiu raster.

So snh X0 vi trung bnh ca tng vng m cc pixel X1, X2, X3,

X4 (hoc X1, X2) thuc v.

Ghp X0 vo vng tng ng nu sai lch l nh.

Chuyn sang pixel tip v lp li bc 2.

Hnh 2.21 Pht trin vng lin kt trng tm (CLRG)


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2.6. Nhn dng nh v nn nh

2.6.1. Nhn dng nh

Nhn dng l qu trnh phn loi cc i tng c biu din theo mt m

hnh no v gn chng mt tn (gn cho i tng mt tn gi, tc l mt dng)

da theo nhng quy lut v mu chun.

Nhn dng nh l giai on cui ca cc h thng x l nh. Nhn dng nh

da trn l thuyt nhn dng (Pattern Recognition). Trong l thuyt v nhn dng

ni chung v nhn dng nh ni ring c ba cch tip cn khc nhau:

Nhn dng da vo phn hoch khng gian.

Nhn dng da vo cu trc.

Nhn dng da vo k thut mng nron.

Nhn chung, mt h thng nhn dng nh u c s tng qut sau:

Hnh 2.22 S tng qut h thng nhn dng nh

2.6.2. Nn nh

Nhm gim thiu khng gian lu tr. c tin hnh theo 2 khuynh hng l

nn c bo ton v nn khng bo ton thng tin. Nn khng bo ton th thng c

kh nng nn cao hn nhng kh nng phc hi km hn. C 4 cch tip cn c bn

trong nn nh:

Nn nh thng k: Da vo vic thng k tn xut xut hin ca gi tr cc

im nh. V d: *.TIF

Nn nh khng gian: Da vo v tr khng gian ca cc im nh tin

hnh m ha. K thut li dng s ging nhau ca cc im nh trong cc

vng gn nhau. V d: *.PCX


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Nn nh s dng php bin i: Tip cn theo hng nn khng bo ton,

do vy k thut thng nn hiu qu hn. V d: *.JPG

Nn nh Fractal: S dng tnh cht Fractal ca cc i tng nh, th hin

s lp li ca cc chi tit. K thut nn s tnh ton ch cn lu tr phn

gc nh v quy lut sinh ra nh theo nguyn l Fractal.

2.7. Cc k thut hu x l

2.7.1. Rt gn s lng im biu din

Rt bt cc im thu c gim thiu khng gian lu tr v thun tin cho

vic so snh.

Bi ton : Cho ng cong gm n im trong mt phng (x1,y2),(x1, y2),

(x1,y2). Hy b bt 1 s im thuc ng cong sao cho ng cong mi nhn c

l (Xi1, Yi1), (Xi2, Yi2), (Xim, Yim) gn ging vi ng cong ban u.

a) Thut ton Douglas Peucker

tng ca thut ton Douglas-Peucker l xt xem khong cch ln nht t

ng cong ti on thng ni hai u mt ng cong c ln hn ngng

khng. Nu ng th im xa nht c gi li lm im chia ng cong v thut

ton c thc hin tng t vi hai ng cong va tm c. Trong trng hp

ngc li, kt qu ca thut ton n gin ho l hai im u mt ca ng cong.

Hnh 2.23: n gin ha ng cng theo thut ton Douglas Peucker

Thut ton Douglas-Peucker:

Chn ngng .

Tm khong cch ln nht t ng cong ti on thng ni hai u

on ng cong h.

Nu h th dng.

Nu h > th gi li im t cc i ny v quay tr li bc 1.


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Nhn xt: Thut ton ny thun li i vi cc ng cong thu nhn c m

gc l cc on thng, ph hp vi vic n gin ho trong qu trnh vc t cc bn

v k thut, s thit k mch in v.v..

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