bundle block adjustment with 3d natural cubic splines

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[12] Chen, T., and R.S. Shibasaki. 1998. Determination of camera’s orientation parametersbased on line features. International Archives of Photogrammetry andRemote Sensing, Hakodate, Japan 32(5),23–28.[13] Chung, K, and L. Shen. 1992. Vectorized algorithm for B-Spline curve fitting onCray X-MP EA/16se. Proceedings of the Supercomputing ’92 conference 166–169.[14] Drewniok, C., and K. Rohr. 1996. Automatic exterior orientation of aerial imagesin urban environments. International Archives of Photogrammetry and RemoteSensing 31(B3),146–152.[15] Drewniok, C., and K. Rohr. 1997. Exterior orientation-an automatic approachbased on fitting analytic landmark models. ISPRS Journal of Phtogrammetryand Remote Sensing 52,132–145.[16] Ebner, H., and T. Ohlhof. 1994. Utilization of ground control points for imageorientation without point identification in image space. International Archivesof Photogrammetry and Remote Sensing 30(2/1),206–211.[17] Föstner, W. 2000. New orientation procedures. International Archives of Photogrammetry,Remote Sensing, and Spatial Information Sciences 33(3),297–304.[18] Föstner, W., and E. Gülch. 1986. A feature based correspondence algorithm forimage matching. International Archives of Photogrammetry and Remote Sensing26(B3/3),13–19.[19] Föstner, W., and E. Gülch. 1987. A fast operator for detection and preciselocation of distinct points, corners and centres of circular features. ISPRS Intercommissionworkshop, Interlaken 149–155.[20] Gülch, E. 1995. Line photogrammetry: a tool for precise localization of 3Dpoints and lines in automated object reconstruction. Integrating PhotogrammetricTechniques with Scene Analysis and Machine Vision II SPIE, Orlando, USA2486,2–12.[21] Gruen, A., and D. Akca. 2005. Least squares 3D surface and curve matching.ISPRS Photogrammetry & Remote Sensing 59,151–174.[22] Guenter, B., and R. Parent. 1990. Computing the arc length of parametriccurves. IEEE Computer Graphics and Applications 10(3),72–78.[23] Guy, G., and G. Medioni. 1996. Inferring global perceptual contours from localfeatures. International Journal of Computer Vision Vol. 20(1-2),113–133.114
[24] Haala, N., and G. Vosselman. 1992. Recognition of road and river patternsby relational matching. International Archives of Photogrammetry and RemoteSensing 29(B3),969–975.[25] Habib, A., M. Morgan, E.M. Kim, and R. Cheng. 2004. Linear features inphotogrammetric activities. XXth ISPRS Congress, Istanbul, Turkey PS ICWGII/IV: Automated Geo-Spatial Data Production and Updating,610.[26] Habib, A.F. 1999. Aerial triangulation using point and linear features. Proceedingsof the ISPRS Conference on Automatic Extraction of GIS Objects fromDigital Imagery, Munich, Germany, 32 32(3-2W5),137–142.[27] Habib, A.F., A. Asmamaw, D. Kelley, and M. May. 2000. Linear features inphotogrammetry. Tech. Rep. 450, Department of Civil and Environmental Engineeringand Geodetic Science, The Ohio State University.[28] Habib, A.F., M. Morgan, and Y.R. Lee. 2002a. Bundle adjustment with selfcalibrationusing straight lines. Photogrammetric Record 17(100),635–650.[29] Habib, A.F., S.W. Shin, and M. Morgan. 2002b. Automatic pose estimationof imagery using free-form control linear features. International Archivesof Photogrammetry, Remote Sensing and Spatial Information Sciences 34(Part3A),150–155.[30] Hannah, M. 1989. A system for digital stereo image matching. PhotogrammetricEngineering and Remote Sensing 55(12),1765–1770.[31] Harric, C., and M. Stephens. 1987. A combined corner and edge detector. 4thAlvey vision conference 147–151.[32] Heikkilä, J. 1991. Use of linear features in digital photogrammetry. PhotogrammetricJournal of Finland 12(2),40–56.[33] Heikkilä, J. 2000. Geometric camera calibration using circular control points.IEEE Transaction on Pattern Analysis and Machine Intelligence Vol. 22,No.10,1066–1077.[34] Heuvel, F. 1999. A line-photogrammetric mathematical model for the reconstructionof polyhedral objects. Videometrics VI, Proceedings of SPIE Vol. 3641,60–71.[35] Jacobs, D. 1996. Robust and efficient detection of salient convex groups. IEEETransactions on Pattern Analysis and Machine Intelligence 18(1),23–37.115
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c○ Copyright byWon Hee Lee2008
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ACKNOWLEDGMENTSThanks be to God, my
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3. BUNDLE BLOCK ADJUSTMENTWITH 3D N
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CHAPTER 1INTRODUCTION1.1 OverviewOn
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y an intersection employing more th
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similarity of geometric properties
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straight linear features or formula
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• Bundle block adjustment by the
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Hessian. Interest point operators w
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[60], Ebner and Ohlhof(1994) [16],
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a complicated problem. The developm
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⎡⎢⎣x i − x py i − y p−f
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x p = −f (X A + t · a − X C )r
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surfaces and terrain models in 2D a
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f(u) − e(u) = g(u)f(u) − e(u) =
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Tankovich[69] used linear features
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(a) 0th order continuity (b) 1st or
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Cardinal splineA Cardinal spline is
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2.3.2 Fourier transformFourier seri
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For other polyline expressions, Aya
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Each segment of a natural cubic spl
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⎡⎢⎣2 11 4 11 4 1· · ·1 4 1
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3.2 Extended collinearity equation
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R −1 = R T . The matrix R T (= R
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dx p = M 1 dX C + M 2 dY C + M 3 dZ
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In this research, the arc-length pa
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=√∫ √√√ ()ti+1−f u′ (
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This equation can be replaced with
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order polynomial using Newton’s d
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y collinearity equations, tangents
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d tan(θ t ) = w′ (v ′ w − w
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y each two points, which are four e
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+M 14 db i3 + M 15 dc i0 + M 16 dc
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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
Page 81 and 82: 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
Page 119 and 120: +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: BIBLIOGRAPHY[1] Ackerman, F., and V
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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