View - Statistics - University of Washington

More documents

Recommendations

Info

168Zahn, C. (1971), “Graph-Theoretical Methods for Detecting and DescribingGestalt Structures,” IEEE Transactions on Computers, C-20,1, 68-86.
Appendix ASOFTWARE DISCUSSIONA.1 XVThe main image segmentation methods discussed in this dissertation have beenadded to a modified version of XV, a popular and widely used UNIX image viewingprogram. XV provides an ideal platform for implementing image processingalgorithms, because code for new algorithms can be added in a relatively modularway. XV has an easy to undertand graphical user interface, with controls for mostaspects of the image display (size, colormap, cropping, and so on). It reads mostcommon image formats. The internal representation of images in XV is the sameregardless of the image file type, which alleviates the need to convert between formats.A few image processing algorithms are built in to XV; these can be accessedusing the “algorithms” button. I have added several new algorithms under thisbutton: threshold, erode, dilate, segment, and autosegment.The threshold algorithm is a simple thresholding of the image based on a userspecifiedthreshold value.Erode and dilate are algorithms which carry out the erode and dilate operationsof mathematical morphology using a user-specified structuring element. Thestructuring element is specified by supplying a file which defines the structuringelement. The most common structuring element is probably the simple 3x3 square;a sample file specifying this structuring element is included in the same directoryas the executable, and the source code includes instructions on how to create a
Page 1:
Fast Automatic Unsupervised Image S
Page 5:
In presenting this dissertation in
Page 9 and 10:
TABLE OF CONTENTSList of FiguresLis
Page 11 and 12:
4.1.2 Penalty Adjustment . . . . .
Page 13 and 14:
Appendix A: Software Discussion 169
Page 15 and 16:
4.1 (a) Signal generated by an AR(1
Page 17 and 18:
5.29 Aerial image of a buoy, before
Page 19:
DEDICATIONThis work is dedicated to
Page 22 and 23:
20 10 20 30 40 50 600 10 20 30 40 5
Page 24 and 25:
4fast because they can be implement
Page 26 and 27:
6multidimensional observations at e
Page 28 and 29:
••••8Figure 2.1(a) is a sim
Page 30 and 31:
10Principal Curve•••••Dat
Page 32 and 33:
12based on the estimates of the par
Page 34 and 35:
14first of these can be done by a h
Page 36 and 37:
162.4 Examples2.4.1 A Simulated Two
Page 38 and 39:
18Figure 2.5: HPCC applied to the t
Page 41 and 42:
21Table 2.2: BIC results for simula
Page 43 and 44:
232.4.3 New Madrid Seismic RegionDa
Page 45 and 46:
25Table 2.3: BIC results for New Ma
Page 47 and 48:
27Latitude35.5 36.0 36.5 37.0 37.5
Page 49 and 50:
29set, so we want to avoid assumpti
Page 51 and 52:
31The examples we have presented in
Page 53 and 54:
Chapter 3MARGINAL SEGMENTATIONIn th
Page 55 and 56:
35about ˜θ; this is a good approx
Page 57 and 58:
373.2 Mixture ModelsThe mixture den
Page 59 and 60:
39for estimating the model paramete
Page 61 and 62:
41The ith pixel of X or C is denote
Page 63 and 64:
43In componentwise classification,
Page 65 and 66:
45ponent. This classification proce
Page 67 and 68:
Chapter 4ADJUSTING FOR AUTOREGRESSI
Page 69 and 70:
49Dependence CaseWhen |β| < 1, the
Page 72 and 73:
52For practical purposes, equation
Page 74 and 75:
54g ′′ (θ) ≈⎛⎜⎝∑ Ni=
Page 76 and 77:
564.1.3 Computing BIC with the AR(1
Page 78 and 79:
58upper, left, and right borders of
Page 80 and 81:
60L(Y −B |M, B) = L IND (Y −B |
Page 82 and 83:
62We now return to equation 4.56 an
Page 84 and 85:
64and then computing a mean-correct
Page 86 and 87:
66regression, which can be written
Page 88 and 89:
68resulting R 2 values are 0.93 for
Page 90 and 91:
700 5 10 15 200 5 10 15 20Figure 4.
Page 92 and 93:
72negative value would mean that ne
Page 94 and 95:
74Once each pixel has been updated,
Page 96 and 97:
76The basic idea of this psuedolike
Page 98 and 99:
78The consistency result presented
Page 100 and 101:
80⎛∑ ⎞Kj=1f(Yg i (Y i ) = log
Page 102 and 103:
82| log(f(Y i |X i = S, θ 1 ))|
Page 104 and 105:
84⇒ (L ˆX (Y |K)) exp(−(D K
Page 106 and 107:
86the object of the expectation is
Page 108 and 109:
88Thus, equation 5.42 holds, so BIC
Page 110 and 111:
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
Page 130 and 131:
1100.0 0.005 0.010 0.015 0.020 0.02
Page 132 and 133:
112three pixels remain incorrectly
Page 134 and 135:
114Table 5.3: EM-based parameter es
Page 136 and 137:
1160 10 20 30 40 50 600 10 20 30 40
Page 138 and 139: 1180 10 20 30 40 50 600 10 20 30 40
Page 140 and 141: 1205.3.3 Ice FloesFigure 5.10 shows
Page 142 and 143: 1220 20 40 60 80 1000 20 40 60 80 1
Page 144 and 145: 124Percent0.0 0.005 0.010 0.015 0.0
Page 146 and 147: 1260 20 40 60 80 1000 20 40 60 80 1
Page 148 and 149: 1280 20 40 60 80 1000 20 40 60 80 1
Page 150 and 151: 1305.3.4 Dog LungFigure 5.17 presen
Page 152 and 153: 132Table 5.6: Logpseudolikelihood a
Page 154 and 155: 134Percent0.0 0.05 0.10 0.15 0.200
Page 156 and 157: 1360 20 40 60 80 100 1200 20 40 60
Page 158 and 159: 1380 20 40 60 80 100 1200 20 40 60
Page 160 and 161: 140interior to the land which had i
Page 162 and 163: 142Table 5.9: EM-based parameter es
Page 164 and 165: 1440 20 40 60 80 100 1200 20 40 60
Page 166 and 167: 1460 20 40 60 80 100 1200 20 40 60
Page 168 and 169: 1485.3.6 BuoyFigure 5.29 is an aeri
Page 170 and 171: 150Table 5.10: Logpseudolikelihood
Page 172 and 173: 1520 20 40 60 80 1000 20 40 60 80 1
Page 174 and 175: 1540 20 40 60 80 1000 20 40 60 80 1
Page 176 and 177: 1560 20 40 60 80 1000 20 40 60 80 1
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 190 and 191: 170structuring element file.Segment
Page 192 and 193: 172A.3 Splus codehpcc - Hierarchica
Page 194: VITA1993 B.S. Mathematics, Harvey M
show all

View - Statistics - University of Washington

Create successful ePaper yourself

Delete template?

Save as template?