+ v′ (u ′ w − w ′ u) − u ′ (v ′ w − v ′ w)r(u ′ w − w ′ u) 2 32 t 2 − v(u′ w − w ′ u) − u(v ′ w − vw ′ )2r(u ′ w − w ′ u) 2 32 tL 14 = (v′ w − w ′ v)(u ′ w − w ′ u) 2 {w′ r 13 t 3 − 3wr 13 t 2 1} −u ′ w − w ′ u {w′ r 23 t 3 − 3wr 23 t 2 }+ v′ (u ′ w − w ′ u) − u ′ (v ′ w − v ′ w)r(u ′ w − w ′ u) 2 33 t 3 − v(u′ w − w ′ u) − u(v ′ w − vw ′ )3r(u ′ w − w ′ u) 2 33 t 2L 15 = w′ (v ′ w − w ′ v)(u ′ w − w ′ u) r w ′2 13 −u ′ w − w ′ u r 23 + v′ (u ′ w − w ′ u) − u ′ (v ′ w − v ′ w)r(u ′ w − w ′ u) 2 33L 16 = (v′ w − w ′ v)1(u ′ w − w ′ u) 2 {w′ r 13 t − wr 13 } −u ′ w − w ′ u {w′ r 23 t − wr 23 }+ v′ (u ′ w − w ′ u) − u ′ (v ′ w − v ′ w)r(u ′ w − w ′ u) 2 33 t − v(u′ w − w ′ u) − u(v ′ w − vw ′ )r(u ′ w − w ′ u) 2 33L 17 = (v′ w − w ′ v)(u ′ w − w ′ u) 2 {w′ r 13 t 2 1− 2wr 13 t} −u ′ w − w ′ u {w′ r 23 t 2 − 2wr 23 t}+ v′ (u ′ w − w ′ u) − u ′ (v ′ w − v ′ w)r(u ′ w − w ′ u) 2 33 t 2 − v(u′ w − w ′ u) − u(v ′ w − vw ′ )2r(u ′ w − w ′ u) 2 33 tL 18 = (v′ w − w ′ v)(u ′ w − w ′ u) 2 {w′ r 13 t 3 − 3wr 13 t 2 1} −u ′ w − w ′ u {w′ r 23 t 3 − 3wr 23 t 2 }+ v′ (u ′ w − w ′ u) − u ′ (v ′ w − v ′ w)(u ′ w − w ′ u) 2 r 33 t 3 − v(u′ w − w ′ u) − u(v ′ w − vw ′ )(u ′ w − w ′ u) 2 3r 33 t 2L 19 = (v′ w − w ′ v)(u ′ w − w ′ u) 2 {w′ [r 11 X ′ i(t) + r 12 Y ′i (t) + r 13 Z ′ i(t)−w[r 11 X ′′i (t) + r 12 Y ′′i (t) + r 13 Z ′′i (t)}1−u ′ w − w ′ u {w′ [r 21 X i(t) ′ + r 22 Y i ′ (t) + r 23 Z i(t)′−w[r 21 X ′′i (t) + r 22 Y ′′i (t) + r 23 Z ′′i (t)}+ v′ (u ′ w − w ′ u) − u ′ (v ′ w − v ′ w)(u ′ w − w ′ u) 2 {w ′ [r 31 X ′ i(t) + r 32 Y ′i (t) + r 33 Z ′ i(t)}− v(u′ w − w ′ u) − u(v ′ w − vw ′ )(u ′ w − w ′ u) 2 − w[r 31 X ′′i (t) + r 32 Y ′′i (t) + r 33 Z ′′i (t)}112
BIBLIOGRAPHY[1] Ackerman, F., and V. Tsingas. 1994. Automatic digital aerial triangulation.Proceedings of ACSM/ASPRS annual convention 1,1–12.[2] Akav, A., G.H. Zalmanson, and Y. Doytsher. 2004. Linear feature based aerialtriangulation. XXth ISPRS Congress, Istanbul, Turkey Commission III.[3] Andrew, A.M. 2002. Homogenising Simpson’s rule. Kybernetes 31(2),282–291.[4] Ayache, N., and O. Faugeras. 1989. Maintaining representation of the environmentof a mibile robot. IEEE Transactions on Robotics Automation 5(6),804–819.[5] Bartels, R.H., J.C. Beatty, and B.A. Barsky. 1998. Hermite and <strong>cubic</strong> splineinterpolation. San Francisco, CA, Hilger,9-17.[6] Berengolts, A., and M. Lindenbaum. 2001. On the performance of connectedcomponents grouping. International Journal of Computer Vision 41(3),195–216.[7] Besl, P., and N. McKay. 1992. A method for registration of 3-D shapes. IEEETrans on Pattern Analysis and Machine Intelligence 14(2),239–256.[8] Boyer, K.L., and S. Sarkar. 1999. Perceptual organization in computer vision:status, challenges and potential. Computer Vision and Image UnderstandingVol. 76(1),1–5.[9] Canny, J. 1986. A computational approach to edge detection. IEEE Transactionon Pattern Analysis and Machine Intelligence Vol. 8,679–698.[10] Casadei, S., and S. Mitter. 1998. Hierarchical image segmentation- detection ofregular curves in a vector graph. International Journal of Computer Vision Vol.27(1),71–100.[11] Catmull, E., and R. Rom. 1974. A class of local interpolating <strong>splines</strong>. Computeraided geometric design, R.E. Barnhill and R.F. Riesenfeld, R.F. Eds., New York,NY, Academic Press,317-326.113
- 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 and 18:
similarity of geometric properties
- Page 19 and 20:
straight linear features or formula
- 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
- 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: 1−u ′ w − w ′ u {w′ [s 21
- 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