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142Table 5.9: EM-based parameter estimates for the Washington coast image.8 segments.Means: 105.09 87.29 154.81 184.37 223.56 78.79 55.71 67.53SDs : 25.61 6.6 14.62 14.65 17.39 5.52 2.25 4.63Probs: 0.125 0.173 0.018 0.014 0.006 0.187 0.354 0.1237 segments.Means: 112.1 87.56 153.29 141.34 78.98 55.71 67.52SDs : 15.71 7.07 21.44 46.16 5.62 2.27 4.65Probs: 0.052 0.188 0.015 0.065 0.197 0.356 0.1286 segments.Means: 114.08 85.85 152.2 182.73 74.36 55.66SDs : 15.8 7.65 16.96 31.42 10.23 2.17Probs: 0.069 0.186 0.025 0.026 0.361 0.3345 segments.Means: 113.01 86.11 179.14 74.1 55.67SDs : 26.91 7.26 31 9.61 2.18Probs: 0.114 0.181 0.03 0.338 0.3364 segments.Means: 120.1 78.8 178.59 55.68SDs : 25.04 11.08 31.64 2.19Probs: 0.089 0.542 0.03 0.3383 segments.Means: 132.05 79.07 55.69SDs : 39.48 11.09 2.19Probs: 0.123 0.539 0.3382 segments.Means: 130.82 70.04SDs : 39.83 14.44Probs: 0.126 0.8741 segments.Means: 77.69SDs : 28.08Probs: 1
143Percent0.0 0.02 0.04 0.06 0.080 50 100 150 200 250GreyscaleFigure 5.24: Marginal histogram of the Washington coast image, with the estimated 6 componentmixture density.
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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
Page 112 and 113: 92N log(σ K ) − N log(σ 1 ) −
Page 114 and 115: 94final number of clusters. The cho
Page 116 and 117: 96ization to initialize (see sectio
Page 118 and 119: 98ˆσ 2 j =∑ Ni=1 ˆQ ij (Y i
Page 120 and 121: 100one particular data value, which
Page 122 and 123: 102classification they are not. Eac
Page 124 and 125: 1045.2.6 Morphological Smoothing (O
Page 126 and 127: 1065.3 Image Segmentation Examples5
Page 128 and 129: 1080 10 20 30 400 10 20 30 40Figure
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Page 132 and 133: 112three pixels remain incorrectly
Page 134 and 135: 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 150 and 151: 1305.3.4 Dog LungFigure 5.17 presen
Page 152 and 153: 132Table 5.6: Logpseudolikelihood a
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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 186 and 187: 166Maps,” Biological Cybernetics,
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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