ECE 194C Target Classification in Sensor Networks Problem. Fundamental problem in pattern recognition.

Transcription

1 ECE 94C arget Classfcato Sesor Netorks.ece.ucsb.edu/Facult/Ilts/ece94c Proble Gve sgature of a target, e.g. sesc, acoustc, vdeo. Detere hch categor the sgature belogs to Fudaetal proble patter recogto. Desg classfers to use teporal, FF, autoregressve odel paraeters to ze error rate.

2 Sesc arget Sgatures Ref: L, Wog, Hu ad Saeed Detecto, Classfcato ad rackg of argets, IEEE Sgal Proc. Mag.. Sesoeter FF N e πk / N Poer spectral dest PSD

3 Vdeo Sesg Pedestra Avodace Ref. D. Gavrla, Sesor-based pedestra protecto, IEEE Itellget Sst. Daler-Chrsler Chafer Sste Proble s to detect pedestras cluttered ager. Classf pedestra vs. tree vs. buldg vs. car. 3

4 Brdcall Classfcato Ref: Wag, Estr, Grod, Preprocessg for habtat otorg, EURASIP Joural o Appled Sg. Proc. 3 4

5 5 Baes Classfer Optal classfer to ze probablt of classfcato error. a arg ˆ P ω ω ω /, N N k e k X N X X X π Feature vector eaple -- PSD Classfer ωˆ

6 6 Baes Classfer Gaussa Dstrbuto Specal case Assue s Gaussa th ko ea vector for each class ad costat covarace. Use Baes rule l arg ˆ ep / N N P P c P P P P ω ω ω ω ω ω σ σ π ω ω ω

7 Baes Classfer Ko Gaussa Dstrbuto Equal Prors Matlab voroo, plots Voroo regos. Rego s the set of pots closer to, the a other pot. Pots, are eas of each categor for the Gaussa case. ω ω Classf as ω for all ths rego. ω N N 7

8 Nearest-Neghbor Classfer Assue N prototpe vectors are avalable th ko classfcato e.g. sesc FFs of ko vehcles. 3 3 N N N3 3 ω ω 3 For each test vector, fd prototpe that s closest ad classf as assocated ω ˆ ω arg, ω ω 8

9 Nearest-Neghbor Classfer ω ω ω 3 ω ω 3 ω ω 3 3 9

10 Gaussa Classfer/K-Meas Algorth Assue prototpes ad test vectors coe fro the sae ture Gaussa dest. ep, / M k k N p c p p p ω σ σ π ω ω ω... # # ˆ N p ω Splest estate of pror probablt of class s relatve frequec of prototpes class.

11 K-Meas Algorth Proble: Prototpes for M clusters class. For Gaussa ture odelg, at to fd best separato betee clusters to choose ea vectors.

12 K-Meas Algorth th a Class Step : Place k pots radol to space represeted b class. hese are the tal cetrods Step : Assg each to group hose cetrod s closest. Step 3: Recopute cetrods

13 Matlab keas, Fucto Pots are rad5,, rad5,. Note cetrods are close to true eas [,] ad [,]. Cetrods 3

14 4 Gaussa Mture Classfer Step : Ru k-eas algorth o prototpes. Step : Set Gaussa ea vectors to cetrods. Step 3: Copute covarace atr of prototpes correspodg to each cetrod. j all for p p f j M k j k N M k k N > ep ep, /, / ω σ σ π ω σ σ π ω

15 Neural Netork Iterpretato of Gaussa Mture Classfer Σ ωˆ > < Σ ep ep ep ep j j 5

16 Eaples of Gaussa Mture Classfcato Desoal Feature Vector

17 Sesc Classfcato Results Ref: L, Wog, Hu ad Saeed Detecto, Classfcato ad rackg of argets, IEEE Sgal Proc. Mag.. 7

18 8 Dstace Metrc Eucldea Dstace Vs. Noralzed Correlato ' ' ' ' ' ' ' ' ' ', ' N N N ρ

19 Cross-Correlato Classfcato Brd call eaples Loo Loo 4 Loo usg specgralooa,56,fs Loo 4 9

20 Autocorrelato Loo,,,, j j N N N N ρ ρ a

21 Cross-Correlato Loos ad 4 Use Matlab corr fucto o spectrogra age. Noralzed cross-correlato coeffcet.,,,,, j j N N N N N N ρ ρ a.567 B specgraloo,56,fs B absb corrb,b/orb, fro * orb, fro

22 Cross-Correlato Loo/Hero Hero Loo

23 Cross-Correlato Loo/Hero ρ a.49 3

24 Support Vector Mache for SAR Classfcato Ref: Q. Zhao ad J. Prcpe Support vector aches for SAR autoatc target recogto, IEEE ras. AES SAR agg.sada.gov/radar 4

25 SAR Iagg/Pose Ref: Q. Zhao ad J. Prcpe Support vector aches for SAR autoatc target recogto, IEEE ras. AES Proble: Classf tpe of object 7 ak th oretato ucertat. 7 BR7 BMP 5

26 o-class Perceptro Learg Prevous clusterg techques dd ot ze a error etrc. Use supervsed perceptro learg to classf. Assue trag set s avalable of vectors e.g. SAR ages, brd calls, fgerprts, etc. o-class proble.e. ak, ot-ak { {,,,,...,,..., } } th age Perceptro ca classf learl separable prototpes Roseblatt, 957 g sg b 6

27 Perceptro Archtecture N g sg b Σ Σ b N 3 7

28 Perceptro Crtera sg b sg b b Mze J b E 8

29 9 Perceptro Learg Rule Defe error due to e put. Note b s lke a eght th ut put. > <,,,,, sg R b R b classfed correctl b b t e sg sg b t b b b t

30 Perceptro Learg b R R R b b b s R, but t. e 3

31 Matlab Perceptro Deo NN oolbo Neural Netork DESIGN Decso Boudares W Move the perceptro decso boudar b draggg ts hadles. r to dvde the crcles so that oe of ther edges are red. he eghts ad bas ll take o values assocated th the chose boudar. Drag the hte ad black dots to defe dfferet probles W.55.7 b -. Chapter 4 3

32 Matlab Peceptro Feature Space Classfcato Neural Netork DESIGN Perceptro Classfcato Iput Space W [ ] b p [.78;.85;-.6] Clck [Go] to sed a frut do the belt to be classfed b a perceptro etork. eght a hardlsw*pb he calculatos for the perceptro ll appear to the left. - a hardls.85 a teture - - shape Frut Apple SHAPE:.78 EXURE:.85 WEIGH: -.6 Frut Neural Netork Orages Apples Chapter 3 3

33 Neural Netork Mea-Square Error Mzato J g, Class.5 g, ep b Class - /ep Proble: he NN should lear based o prototpes. Learg eas selectg the ad b vectors to ze J based o adaptato to prototpes ad desred resposes. 33

34 34 Delta Rule Backpropagato for MSE Classfer b b e e g J b g g J,, ep,, δ µ µ µ

35 Neural Netork MSE Classfer Sgle Laer > < Dec. Σ ep u Σ b ± Adapt Delta-rule 35

36 Support Vector Mache g sg αk, SV b, R R,, Eaple kerel fuctos K, ep σ 36

37 Support Vector Mache ωˆ > α Σ < α α N N Σ α N N ep ep ep ep N N 37

38 38 Optzato for SVM Proble s to select alpha coeffcets to aze separato betee classes, hle keepg fed error rates. Proble cove optzato, Lagraga pral ad duals. Costrat sg, sg Mze b b b K g SV SV φ φ α φ α SV SV Soluto α φ α

39 ADARON Learg Rule for SVM f M AD Whle 3 Choose 5 If 6 jα jk, j f AD Italze α, η b Ed M AD 4 δ η α δ f Whle AD j < t patter AD threshold >, α α δ, j b t b b δ Ideall should be, or >. Zero f f AD lke perceptro rule res to drve f AD to. 39

40 SVM for SAR Iager Ref: Q. Zhao ad J. Prcpe Support vector aches for SAR autoatc target recogto, IEEE ras. AES SVM 7 hreshold est Clutter Rejecto, SVM BR7 hreshold est Clutter Rejecto, 8 8 pel SAR age pose corrected SVM BMP hreshold est Clutter Rejecto, 4

41 SAR Iager Cofuso Matrces Ref: Q. Zhao ad J. Prcpe Support vector aches for SAR autoatc target recogto, IEEE ras. AES 4

42 Probles Iage Classfcato Color Correct for oretato pose, traslato, scale. Use varat trasfors, e.g. FF agtude s varat to traslato phase shft. Feature etracto detf object backgroud Choce of earest-eghbor, k-eas clusterg/baes classfer, or eural-et ethods. Log trag tes for ages. 4

43