bundle block adjustment with 3d natural cubic splines

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intersect in a point. Possible constraint for coplanarity equations is that conjugatepoints are somewhere on the epipolar lines for point matching.Point-based procedure relationship between point primitives is most widely developedin traditional photogrammetry, that one measured point on an image is identifiedon another image. Even for linear features, data for the stereo model in softcopy workstationis collected by points so that further application and analysis relies on a pointas primary input data. The coefficients of interior, relative and absolute orientationare computed from point relationship. Interior orientation compensates lens distortion,film shrinkage, scanner error and atmosphere refraction. Relative orientationmakes the stereoscopic view possible, and the relationship between model coordinatesystem and object space coordinate system is reconstructed by absolute orientation.Using GCPs is widely employed to compute the orientation parameters. Althoughemploying a lot of GCPs is a time consuming procedure and blocks the robust andaccurate automation what research on digital photogrammetry aims, developing ofa computer, storage capacity, photogrammetric software and a digital camera canreduce the computational and time complexity.In traditional photogrammetric activities, point-based methods with experiencedhuman operators are processed well but not the autonomous environment of digitalphotogrammetry.Although some reasonable approaches have been proven in digitalphotogrammetry, the object recognition, the feature extraction and matchingprocedures are a challenging task in the computer environment of the autonomousphotogrammetry. To develop more robust and accurate techniques, linear objects of6
straight linear features or formulated entities such as conic sections, which are accommodatingelements, other than points that are adopted instead of points in aerialtriangulation.Even though recent advanced algorithms provide accurate and reliable linear featureextraction, extracting linear features is more difficult than extracting a discreteset of points which can consist of any form of curves. Control points which are theinitial input data, and break points which are end points of piecewise curves, areeasily obtained with manual digitizing, edge operators or interest operators. A curvecan be represented analytically by seed points which are extracted automatically bythe point extraction in digital imagery.Employing high level features increases the feasibility of geometric informationand provides an analytical and suitable solution for the advanced computer technology.With the development of extraction, segmentation, classification and recognitionof features, the input data for feature-based photogrammetry has been expandedto increase redundancy of aerial triangulation. Since the identification, formulationand application of reasonable linear features is a crucial procedure for autonomousphotogrammetry, the higher order geometric feature-based modeling plays an importantrole in modern digital photogrammetry. The digital image format is suited toautonomous photogrammetry’s purpose, especially the feature extraction and measurement,and it is useful for precise and rigorous modeling of features from images.1.2 Scope of dissertationOver the years, many researchers have investigated the feasibility of linear featuresto autonomous photogrammetry, but the modeling of linear features are limited into7
Page 2: c○ Copyright byWon Hee Lee2008
Page 7 and 8: ACKNOWLEDGMENTSThanks be to God, my
Page 10 and 11: 3. BUNDLE BLOCK ADJUSTMENTWITH 3D N
Page 13 and 14: CHAPTER 1INTRODUCTION1.1 OverviewOn
Page 15 and 16: y an intersection employing more th
Page 17: similarity of geometric properties
Page 21 and 22: • Bundle block adjustment by the
Page 23 and 24: Hessian. Interest point operators w
Page 25 and 26: [60], Ebner and Ohlhof(1994) [16],
Page 27 and 28: a complicated problem. The developm
Page 29 and 30: ⎡⎢⎣x i − x py i − y p−f
Page 31 and 32: x p = −f (X A + t · a − X C )r
Page 33 and 34: surfaces and terrain models in 2D a
Page 35 and 36: f(u) − e(u) = g(u)f(u) − e(u) =
Page 37 and 38: Tankovich[69] used linear features
Page 39 and 40: (a) 0th order continuity (b) 1st or
Page 41 and 42: Cardinal splineA Cardinal spline is
Page 43 and 44: 2.3.2 Fourier transformFourier seri
Page 45 and 46: For other polyline expressions, Aya
Page 47 and 48: Each segment of a natural cubic spl
Page 49 and 50: ⎡⎢⎣2 11 4 11 4 1· · ·1 4 1
Page 51 and 52: 3.2 Extended collinearity equation
Page 53 and 54: R −1 = R T . The matrix R T (= R
Page 55 and 56: dx p = M 1 dX C + M 2 dY C + M 3 dZ
Page 57 and 58: In this research, the arc-length pa
Page 59 and 60: =√∫ √√√ ()ti+1−f u′ (
Page 61 and 62: This equation can be replaced with
Page 63 and 64: order polynomial using Newton’s d
Page 65 and 66: y collinearity equations, tangents
Page 67 and 68: d tan(θ t ) = w′ (v ′ w − w
Page 69 and 70:
y each two points, which are four e
Page 71 and 72:
+M 14 db i3 + M 15 dc i0 + M 16 dc
Page 73 and 74:
collinearity model are described in
Page 75 and 76:
[ ] [ ] [ ]N11 N 12 ˆξ1 c1N12T =N
Page 77 and 78:
systematic errors in the image spac
Page 79 and 80:
interval based on the normal distri
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1 ∂Φ2 ∂l= (X C + d 1 l − a i
Page 83 and 84:
about splines, their relationships,
Page 85 and 86:
cubic spline in the image and the o
Page 87 and 88:
The redundancy budget of a tie poin
Page 89 and 90:
of bundle block adjustment is requi
Page 91 and 92:
ξ kiSP = [ da i0 da i1 da i2 da i3
Page 93 and 94:
Spline location parametersImage 1 I
Page 95 and 96:
Spline location parametersImage 1 I
Page 97 and 98:
5.3 Recovery of EOPs and spline par
Page 99 and 100:
Table 5.7 expressed the convergence
Page 101 and 102:
Iteration with an incorrect spline
Page 103 and 104:
Vertical aerial photographData 9 Ju
Page 105 and 106:
All locations are assumed as on the
Page 107 and 108:
of the Gauss-Markov model correspon
Page 109 and 110:
estimation is obstacled by the corr
Page 111 and 112:
Interior orientation defines a tran
Page 113 and 114:
+ fu ( w2 31 (X i (t) − X C ) + s
Page 115 and 116:
A.2 Derivation of arc-length parame
Page 117 and 118:
+2f( t [1 + t 2) − 1 22s 12 (Y i
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+Du ′ ( t 1 + t 22)2r 11 t + Dv
Page 121 and 122:
A 17 = t [2 − t 1 16 2 f(t 1) −
Page 123 and 124:
1−u ′ w − w ′ u {w′ [s 21
Page 125 and 126:
BIBLIOGRAPHY[1] Ackerman, F., and V
Page 127 and 128:
[24] Haala, N., and G. Vosselman. 1
Page 129 and 130:
[49] Parian, J.A., and A. Gruen. 20
Page 131:
[73] Vosselman, G., and H. Veldhuis
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bundle block adjustment with 3d natural cubic splines

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