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166Maps,” Biological Cybernetics, vol. 43, pp. 59-69.Kundu, A. (1990), “Robust Edge Detection,” Pattern Recognition, 23, 423-440.Latham, G., and Anderssen, R. (1994), “Assessing Quantification for the EMalgorithm,” Linear Algebra and its Applications, 210, Oct, 89-122.Latham, G. (1995), “Existence of EMS Solutions and A-Priori Estimates,” SIAMJournal on Matrix Analysis and Applications, 16, 3, 943-953.LeBlanc, M., and Tibshirani, R. (1994), “Adaptive Principal Surfaces,” Journalof the American Statistical Association, 89, 425, 53-64.Letts, P.A. (1978), ”Unsupervised Classification in the Aries Image Analysis System,”Proceedings of the 5th Canadian Symposium on Remote Sensing, 61-71.Lu, W. (1995), “The Expectation-Smoothing Approach for Indirect Curve Estimation,”Unpublished manuscript.Masson, P., and Pieczinsky, W. (1993), ”SEM Algorithm and Unsupervised StatisticalSegmentation of Satellite Images,” IEEE Transactions on Geoscienceand Remote Sensing, 31(3), 618-633.Murtagh, F. (1995) “Interpreting the Kohonen Self-Organization Feature MapUsing Contiguity Constrained Clustering” Pattern Recognition Letters, vol.16, pp. 399-408.Nychka, D. (1990), “Some Properties of Adding a Smoothing Step to the EMAlgorithm,” Statistics and Probability Letters, 9, 187-193.Oppenheim, A. V., Willsky, A. S., and Young, I. T. (1983), Signals and Systems,Prentice Hall: Englewood Cliffs, New Jersey.Pal, N., and Pal, S. (1993) “A Review on Image Segmentation Techniques,”Pattern Recognition, 26, 1277-1294.Priestley, M.B. (1981) Spectral Analysis and Time Series, Academic Press: NewYork.
167Prim, R. (1957), “Shortest Connection Networks and some Generalizations,” BellSystem Technical Journal, 1389-1401.Raftery, A.E. (1995), “Bayesian Model Selection in Social Research,” in SociologicalMethodology 1995, ed. P.V. Marsden, Blackwells: Cambridge, 111-163.Richards, J.A. (1986), Remote Sensing Digital Image Analysis, Springer-Verlag:New York.Roeder, K. and Wasserman, L. (1997), “Practical Bayesian Density EstimationUsing Mixtures of Normals,” Journal of the American Statistical Association,92, 894-902.Schwarz, G. (1978), “Estimating the Dimension of a Model,” The Annals ofStatistics, 6, 461-464.Serra, J. (1982) Image Analysis and Mathematical Morphology, Academic Press:New York.Silverman, B., Jones, M., Wilson, J., and Nychka, D. (1990), “A Smoothed EMApproach to Indirect Estimation Problems, with Particular Reference toStereology and Emission Tomography (with Discussion),” Journal of theRoyal Statistical Society, Series B, 52, 271-324.Steger, C. (1998) “An Unbiased Detector of Curvilinear Structures,” IEEE Transactionson Pattern Analysis and Machine Intelligence, vol. 20, pp. 113-125.Tibshirani, R. (1992), “Principal Curves Revisited,” Statistics and Computing,2, 183-190.Tibshirani, R. and Hastie, T. (1987), “Local Likelihood Estimation,” Journal ofthe American Statistical Association, 82, 559-568.Ward, J. (1963), “Hierarchical groupings to optimize an objective function,” Journalof the American Statistical Association, 58, 234–244.Wold, S. (1974), “Spline Functions in Data Analysis,” Technometrics, 16, 1-11.
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Fast Automatic Unsupervised Image S
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In presenting this dissertation in
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TABLE OF CONTENTSList of FiguresLis
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4.1.2 Penalty Adjustment . . . . .
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Appendix A: Software Discussion 169
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4.1 (a) Signal generated by an AR(1
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5.29 Aerial image of a buoy, before
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DEDICATIONThis work is dedicated to
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20 10 20 30 40 50 600 10 20 30 40 5
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4fast because they can be implement
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6multidimensional observations at e
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••••8Figure 2.1(a) is a sim
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10Principal Curve•••••Dat
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12based on the estimates of the par
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14first of these can be done by a h
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162.4 Examples2.4.1 A Simulated Two
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18Figure 2.5: HPCC applied to the t
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21Table 2.2: BIC results for simula
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232.4.3 New Madrid Seismic RegionDa
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25Table 2.3: BIC results for New Ma
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27Latitude35.5 36.0 36.5 37.0 37.5
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29set, so we want to avoid assumpti
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31The examples we have presented in
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Chapter 3MARGINAL SEGMENTATIONIn th
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35about ˜θ; this is a good approx
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373.2 Mixture ModelsThe mixture den
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39for estimating the model paramete
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41The ith pixel of X or C is denote
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43In componentwise classification,
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45ponent. This classification proce
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Chapter 4ADJUSTING FOR AUTOREGRESSI
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49Dependence CaseWhen |β| < 1, the
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52For practical purposes, equation
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54g ′′ (θ) ≈⎛⎜⎝∑ Ni=
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564.1.3 Computing BIC with the AR(1
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58upper, left, and right borders of
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60L(Y −B |M, B) = L IND (Y −B |
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62We now return to equation 4.56 an
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64and then computing a mean-correct
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66regression, which can be written
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68resulting R 2 values are 0.93 for
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700 5 10 15 200 5 10 15 20Figure 4.
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72negative value would mean that ne
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74Once each pixel has been updated,
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76The basic idea of this psuedolike
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78The consistency result presented
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80⎛∑ ⎞Kj=1f(Yg i (Y i ) = log
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82| log(f(Y i |X i = S, θ 1 ))|
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84⇒ (L ˆX (Y |K)) exp(−(D K
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86the object of the expectation is
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88Thus, equation 5.42 holds, so BIC
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90the inequality in equation 5.55 h
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92N log(σ K ) − N log(σ 1 ) −
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94final number of clusters. The cho
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96ization to initialize (see sectio
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98ˆσ 2 j =∑ Ni=1 ˆQ ij (Y i
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100one particular data value, which
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102classification they are not. Eac
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1045.2.6 Morphological Smoothing (O
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1065.3 Image Segmentation Examples5
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1080 10 20 30 400 10 20 30 40Figure
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1100.0 0.005 0.010 0.015 0.020 0.02
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112three pixels remain incorrectly
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114Table 5.3: EM-based parameter es
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Page 140 and 141: 1205.3.3 Ice FloesFigure 5.10 shows
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Page 144 and 145: 124Percent0.0 0.005 0.010 0.015 0.0
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Page 150 and 151: 1305.3.4 Dog LungFigure 5.17 presen
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Page 160 and 161: 140interior to the land which had i
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Page 168 and 169: 1485.3.6 BuoyFigure 5.29 is an aeri
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Page 178 and 179: Chapter 6CONCLUSIONSThis dissertati
Page 180 and 181: 160of subsampling would have to be
Page 182 and 183: REFERENCESAllard, D., and Fraley, C
Page 184 and 185: 16494, 555-568.Fraley, C., and Raft
Page 188 and 189: 168Zahn, C. (1971), “Graph-Theore
Page 190 and 191: 170structuring element file.Segment
Page 192 and 193: 172A.3 Splus codehpcc - Hierarchica
Page 194: VITA1993 B.S. Mathematics, Harvey M
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