Objectives. Image R 1. Segmentation. Objects. Pixels R N. i 1 i Fall LIST 2

Transcription

1 Image Segmenaon

2 Obecves Image Pels Segmenaon R Obecs R N N R I -Fall LIS

3 Ke Problems Feaure Sace Dsconnu and Smlar Classfer Lnear nonlnear - fuzz arallel seral -Fall LIS 3

4 Feaure Eracon Image Sace Feaure Sace Sgnfcan Feaures Dfferen Obecs Same Obecs Dsconnu Smlar -Fall LIS 4

5 Feaures Gra level Dsconnu of gra level eure Sascal feaures -Fall LIS 5

6 Image Value sgnfcan -Fall LIS 6

7 Insgnfcan -Fall LIS 7

8 Dervave oeraor -Fall LIS 8

9 Graden f f f Lalacan f f f f Zero-crossng f f Amlude Drecon -Fall 9 LIS

10 LoG Lalacan of a Gaussan f * h f r e r h r 4 r e r r h h f f -Fall LIS

11 hr h r h r LoG σ=. -Fall LIS

12 Imlemenaon of Dervave oeraors Graden f f f f f f f f f f f f f f f f f f h f h h h h Robers h h Sobel -Fall LIS

13 Rober Magnude Orgnal Image Sobel Magnude -Fall LIS 3

14 Lalacan f f f f f f f f f f f f f f f f f f f f 4f f hf h 4 h 8 f f f -Fall LIS 4

15 eure Perodc eure Fourer Secrum -Fall LIS 5

16 sascal feaures z : normalzed hsogram momen n n z z m z m z z z z z zz m z z z m z z enro Ez z ln z -Fall LIS 6

17 gra-level co-occurrence mar oson oeraor P 4 3 [C] on robabl -Fall LIS 7

18 Classfer Lnear Classfer Nonlnear Classfer - Classfer Fuzz Classfer Seral Algorhm Parallel Algorhm -Fall LIS 8

19 hresholdng

20 hresholdng g f oherwse Df Msclassfcaon Error e counf AB counf BA / N f A B : Msclassfcaon A B f B A : Msclassfcaon B A N : oal Number of Pels -Fall LIS

21 Lnear Classfer Lnear Funcon Feaure Sace D Feaure Sace D Feaure Sace -Fall LIS

22 Mnmzaon of msclassfcaon L A B f A f B L e e L B A A B A B = A + B e e e AB L e A mn BA B -Fall LIS

23 Valle hresholdng Kes. B-eak Feaure. Smoohng Hsogram -Fall LIS 3

24 Evaluaon of Msclassfcaon A B ' ' B L A e' L A B B A B B A L A e' L B A B A e' e ' e -Fall 4 LIS

25 Baes Classfer w w P w w P w / P w P w w w k f ma w w k -Fall 5 LIS

26 Osu s hresholdng Mehod -Fall LIS 6 A B A: < B: P P B B A A w mn w w A P L B P A A P L B B P A A A P L B B B P

27 Mamal Enro Classfer H H A: < B: A B A A ln A L B Bln B A B / / L HA HB / P / P P ma HA HB H P HL H P ln P ln P H ln P= -Fall LIS 7

28 Cluserng

29 -Fall LIS 9

30 K-means Cluserng Algorhm: Se : Choose nal cluser ceners Se : Dsrbue mage els among clusers Se 3: Comue new cluser ceners Se 4: Reea ses and 3 unl converges. -Fall LIS 3

31 Arfcal Neural Nework erceron Non-lnear Classfcaon w w X w d n w w n + f d > - f d < n w n+ -Fall LIS 3

32 Acve Conour

33 Snakes: Paramerc Deformable Models -Fall LIS 33

34 Kass Snake aramerc curve : X s s s' s [] acve conour : E snake E snake X s ds mn energ : E X s En X s E X s E X s snake mage con Inernal energ Eernal energ M.Kass A.Wkn and D.erzooulos Snakes:Acve Conour Models Inernaonal Journal of ComuerVson Fall LIS 34

35 Inernal and Eernal Energes E n X s s X s s s Xss s E m age X s w enson flebl lne E lne w edge E Sffness rgd edge w erm E erm E e X s E X s E X s mage con -Fall LIS 35

36 -Fall LIS 36 s s s s s E F ss s e ss s X X X X X X mn ds F E ss s snake X X X Euler-Lagrange equaon: s F s F F ss X s X X ; ; s s F s s F s E F ss X s X e X ss s X X X X ssss ss e E X X from s ndeenden

37 E e X X ss ssss F n s Fe s F F Kass eernal force: n e X s s s s Ee X s E e X s 4 s 4 G I Normalzaon: F e E E e e ma -Fall LIS 37

38 Eernal Energes -Fall LIS 38 I E e I G E e I E e I G E e o edge: o lne:

39 Numercal Imlemenaon E e X X ss ssss E e G I X s X s X s ss ssss E e -Fall LIS 39

40 Graden Vecor Flow Snake Kass snakes: X s X s X s ss ssss E e GVF snakes: X s X s X s ss ssss V graden vecor flow feld C.Xu J.L.Prnce Snakes shaes and graden vecor flow IEEE ransacons on Image Processng Fall LIS 4

41 Graden Vecor Flow -Fall LIS 4 mn dd f f v v u u V E f e ' v u V f f f v v f f f u u

42 Numercal Imlemenaon -Fall LIS 4 f f f u u u f f f v v v c u b u u c v b v v f f b f b c f b c

43 -Fall LIS 43 n n n n v v v u u u 4 4 v v v v v v u u u u u u c v v v v v r v b v c u u u u u r u b u n n n n n n n n n n n n n n 4 4 c u b u u c v b v v r 4 4 / : r sze resrcon se

44 Insensve o Nose mage -Fall LIS 44

45 snake 抗轮廓细小毛刺及缺陷能力 -Fall LIS 45

46 IEEE ransacons on Paern Analss and Machne Inellgence 7 995: Shae Modelng wh Fron Proagaon: A Level Se Aroach R. Mallad J.A.Sehan B.C.Vemur -Fall LIS 46

47 Problems: F. Insabl. Sngulares 3. oologcal changes 4. dffcules o 3D : s s[ S] -Fall LIS 47

48 z z C Level Se C : d -Fall LIS 48

49 : roagang fron F : vecor normal o ' F F -Fall LIS 49

50 Numercal Imlemenaon n n F n Osher scheme: F F F F :Curvaure 3/ -Fall LIS 5

51 F F F A G Indeenden of geomer deenden on geomer F A F G -Fall LIS 5

52 -Fall LIS 5 G A F F G F A F F M I G M M F F A I G F F k I I G k I I G I e k

53 -Fall LIS 53 eenson of F I ˆ : ˆ I I I F F F ˆ I A F F ˆ G A I F F k

54 Eenson of mage-based seed erms C P Q -Fall LIS 54

55 Algorhm : kˆ I F A F G. for search q on Ψ= ge mage-based seed ˆ. n n k and I n 3. : n 4. reurn o. Renalzaon -Fall LIS 55

56 Narrow-band eenson / δ Renalzaon -Fall LIS 56

57 Algorhm. for nsde narrow band kˆ I. ˆ n n k and I n n 3. olgonal aromaon : 4. reea -3 f n<h_ 5. Renalzaon of sgned dsance funcon Ψ -Fall LIS 57

58 Edge Deecon -Fall LIS 58

59 -Fall LIS 59

60 -Fall LIS 6

61 Regon Growng

62 -Fall LIS 6

63 el aggregaon R : regon R : seed S : smlar mearure for q Ω ff Sq > q R q : new seed -Fall LIS 63

64 seed -Fall LIS 64