CS 534: Computer Vision Model Fitting

Transcription

1 CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust estmaton Mssng values problem EM algorthm CS 534 Model Fttng -

2 Model Fttng Model fttng s a fundamental problem n computer vson Data Model parameters? Fnd the model parameters that best fts the data Optmzaton problem The model can be as smple as a D lne or as complex as 3D artculated object. CS 534 Model Fttng - 3 Fttng Models Issues: What s the model? How to measure a good ft? What s your metrc? Effect of nose on the fttng Multple nstances of the same model (object dfferent models Whch data ponts belong to whch object? How many objects are there? CS 534 Model Fttng - 4

3 Smple example: Fttng a lne n D to a set of pont. The same ssues apply to more complex problems Gven a set of ponts {(x,y } fnd lne parameters Least-squares y = ax + b Fnd a, b whch mnmze ( y ax b Resdual: measure how far s pont from the model K M a x = 1 y b K M A x = b mn A x x - b Lnear least-squares We have seen an example of ths before n calbraton CS 534 Model Fttng - 5 Queston: what does the resdual mean? What are we mnmzng? Fnd a, b whch mnmze ( y ax b (x,y y=ax+b Mnmzes vertcal dstances Very poor model (e.g., We can not ft vertcal/near vertcal lnes (x,a x +b CS 534 Model Fttng - 6

4 Better model x cos θ + y snθ + ρ = 0 y ax + by + c = 0, ρ where a + b = 1 Stll we wll use least-squares to mnmze the resdual: ( ax + by + c ax +by +c s the perpendcular dstance θ x Ths s called total least-squares CS 534 Model Fttng - 7 Maxmum Lkelhood Parameter Estmaton (MLE Fttng as a probablstc nference problem We stll usng the D lne example Assume x s correct (determnstc no errors y has a measurement error that s Normally dstrbuted around true y(x y y y( 1/ ( σ : N ( y( x, σ P( y e x (x,y y=ax+b (x, y(x =ax +b CS 534 Model Fttng - 8

5 Maxmum Lkelhood Parameter Estmaton (MLE Assume x s correct (determnstc no errors y has a measurement error that s Normally dstrbuted around true y(x y y y( 1/ ( σ : N( y( x, σ P( y e x Assume errors are ndependent, standard devatons σ for all ponts Probablty that the data comes from the model: probablty that the data and model predcton are wthn y P N = 1 e y y( x 1/ ( σ y Fnd the parameters (a,b that maxmze P Maxmze the lkelhood P(measurement a,b P(measurement model parameters (x,y y=ax+b (x, y(x =ax +b CS 534 Model Fttng - 9 Maxmum Lkelhood = Least squares P N = 1 e y y( x 1/ ( σ y Ths term s rrelevant (doesn t depend on a,b Log P = N = 1 ( y y( x σ N log y Fnd the parameters (a,b that maxmze P Fnd the parameters (a,b that mnmzes Log P Mnmze: N N ( y y( x = = 1 = 1 ( y ax b Least-squares as we know t CS 534 Model Fttng - 10

6 We can assume that both x and y contans measurement errors Both x, y are probablstc random varables x u + w y = v (x,y N( 0, Σ (u, v CS 534 Model Fttng - 11 MAP estmate Maxmum lkelhood estmate (MLE Maxmze P(measurement model parameters But we are actually nterested n maxmzng P(model parameters measurement So what s the dfference? Bayes Rule P(model measurement = P(measurement model * P(model / P(measurement Posteror probablty measurement lkelhood model pror Measurement probablty Maxmze P(model measurement α P(measurement model * P(model Maxmum A posteror Estmaton (MAP MAP wll be useful f we have reasons to prefer one model over the others,.e., f we have pror knowledge about model parameters. e.g., for lne fttng, certan lne orentatons are most probable than others CS 534 Model Fttng - 1

7 Robustness What s the effect of nosy data (outlers Least-squares estmate (smlarly MLE s extremely senstve to outlers The problem s the sngle pont on the rght; the error for that pont s so large that t drags the lne away from the other ponts CS 534 Model Fttng - 13 M-estmators Least-squares mnmze the resdual: sum of squared dstances (resduals for each data pont. r( ; θ e.g. ( ax + by + c x The resdual for a far away pont (outler s huge Instead, we want to reduce the effect of the resduals for far away ponts How to do that: replace (dstance wth somethng that looks lke (dstance for small dstances, and s about constant for large dstances CS 534 Model Fttng - 14

8 M-estmators How to do that: replace (dstance wth somethng that looks lke (dstance for small dstances, and s about constant for large dstances mnmze ρ( r, σ r ρ( r, σ = r + σ Resdual (dstance for each pont CS 534 Model Fttng - 15 M-estmators mnmze ρ( r, σ ρ( r, σ r = r + σ Ths leads to a nonlnear optmzaton problem Iteratve procedure gven an ntal soluton Can stuck to a local mnma depends on ntal guess The choce of s σ crtcal Rght σ Too small ft s nsenstve to all ponts Just stuck to ntal guess Too Large smlar to LS Outler has bg contrbuton CS 534 Model Fttng - 16

9 M-estmators Rght σ Too small ft s nsenstve to all ponts Just stuck to ntal guess Too Large smlar to LS Outler has bg contrbuton CS 534 Model Fttng - 17 M-estmators The choce of s σ crtcal Start wth bg σ - soluton smlar to least squares. Reduce as σ you go CS 534 Model Fttng - 18

10 RANSAC RANdom SAmple Consensus Searchng for a random sample that leads to a ft on whch many of the data ponts agree Extremely useful concept Can ft models even f up to 50% of the ponts are outlers. Repeat Choose a subset of ponts randomly Ft the model to ths subset See how many ponts agree on ths model (how many ponts ft that model Use only ponts whch agree to re-ft a better model Fnally choose the best ft CS 534 Model Fttng - 19 RANSAC RANdom SAmple Consensus Four parameters n : the smallest # of ponts requred k : the # of teratons requred t : the threshold used to dentfy a pont that fts well d : the # of nearby ponts requred Untl k teratons have occurred Pck n sample ponts unformly at random Ft to that set of n ponts For each data pont outsde the sample Test dstance; f the dstance < t, t s close If there are d or more ponts close, ths s a good ft. Reft the lne usng all these ponts End use the best ft CS 534 Model Fttng - 0

11 Mssng varable problems In many vson problems, f some varables were known the maxmum lkelhood nference problem would be easy fttng; f we knew whch lne each token came from, t would be easy to determne lne parameters segmentaton; f we knew the segment each pxel came from, t would be easy to determne the segment parameters fundamental matrx estmaton; f we knew whch feature corresponded to whch, t would be easy to determne the fundamental matrx etc. Ths sort of thng happens n statstcs, too CS 534 Model Fttng - 1 Mssng varable problems Consder lne fttng: What s gven? pont locatons What s mssng? whch ponts belong to whch lne If we know the lne assgnment for each pont ft the lnes (estmate the parameters usng MLE If we know the parameters of the two lnes we can fgure out the lne assgnment for each pont Chcken and egg problem CS 534 Model Fttng -

12 Mssng varable problems Strategy estmate approprate values for the mssng varables plug these n, now estmate parameters re-estmate approprate values for mssng varables, contnue eg guess whch lne gets whch pont now ft the lnes now reallocate ponts to lnes, usng our knowledge of the lnes now reft, etc. We ve seen ths lne of thought before (k means CS 534 Model Fttng - 3 EM algorthm Expectaton-Maxmzaton Iterate untl convergence: replace mssng varable wth expected values, gven fxed values of parameters (E-step - expectaton fx mssng varables, choose parameters to maxmze lkelhood gven fxed values of mssng varables (M-step - maxmzaton e.g., (lne fttng terate tll convergence: allocate each pont to a lne wth a weght, whch s the probablty of the pont gven the lne reft lnes to the weghted set of ponts Lne assgnment for each pont s assumed to be a mssng value (Hdden varable CS 534 Model Fttng - 4

13 EM algorthm Mxture Model: Probablty of pont x gven lne l Probablty of pont x gven all lnes p x θ ( l p ( x Θ = αl p( x θl l α : probablty of pont x les on lne l mxture weghts The whole parameter set Θ = α, L, α, θ, L, θ ( 1 N 1 N Lkelhood for all ponts p ( X Θ α p( x θ = l l l CS 534 Model Fttng - 5 Example: lne fttng usng EM. Soluton s very senstve to ntal guess (assgnment local mnma. CS 534 Model Fttng - 6

14 Segmentaton wth EM K=,3,4,5 Fgure from Color and Texture Based Image Segmentaton Usng EM and Its Applcaton to Content Based Image Retreval,S.J. Belonge et al., Proc. Int. Conf. Computer Vson, 1998, c1998, IEEE CS 534 Model Fttng - 7 Sources Forsyth and Ponce, Computer Vson a Modern approach: chapters 15,16 R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classfcaton. Wley, New York, nd edton, 000 Sldes by D. Forsyth CS 534 Model Fttng - 8