Full text: Close-range imaging, long-range vision

  
  
  
Figure 5. Left image : corners of sub images are projected on the predefined plane and the enclosed rectangle in object space is 
found. Right image: backprojections of the corners of the rectangle define the size of the virtual image. 
enclosed rectangle of the images is defined. Finally, all pixels 
are resampled and the virtual image is created. Since demand- 
ing accuracy is less important for archaeological documentation 
and the terrain height differences are not large, the influences in 
planimetry and in height are generally small. 
The reference plane may be defined and approximated in two 
different ways: in the first case, a given horizontal plane at a 
specified height or a best fitted plane from a group of 3D points. 
The equation of the plane is given as a,*x a,*y + a; = z and 
with least squares adjustment the best fitting plane is calculated. 
In the second case the intersection of the ray with the plane is 
calculated through Eq. 1. For nonplanar surfaces, a higher order 
polynomial surface may be fitted e.g. a; + a,*x + a*x? t a4 
*x*y + as *y” +a = z and the intersection point of the ray with 
the plane may be found by iterative methods. 
X Xo a, 
Y |=|Yo |+t-|b, (D 
Z Zo C, 
where ,. ap(x, — Xo) * b. (y, — Yo) + cp (z, — Zo) 
apa, +b,b, +b,b, 
(a,,b,,c;) » 1=Lp direction vector of ray and normal vector of 
  
plane 
Xo,Yo,Zo camera position 
XprVpaZp Point that lies on the plane 
The version using a best fitted plane was selected as a compro- 
mise between accuracy and computation time. Compared to the 
horizontal plane the surface was better approximated and com- 
pared to the higher polynomial surface it was faster. The plane 
has been selected, so that the residuals of all 3D points were 
minimum (Fig. 6). 
0.8 - 
0.6 
0.4 
CR 
* 
0.2 
  
103 102 ” 106 
y 
Figure 6. Plane fitted over the 3D points. 
When generating the virtual image the corrections resulting 
from lens distortion have to be calculated. The Brown model 
(Eq. 2), modeling radial and descentering distortion gives the 
correction from distorted to the undistorted image, as the input 
are the coordinates of the distorted image. The inverse trans- 
formation corrections have to be computed by solving a two 
dimensional non-linear system of equations with an iterative 
method, e.g Newton Raphson. The corrections are given regard- 
ing to the distorted coordinates, therefore undistorted coordi- 
nated can not be used directly as input in the equation (see be- 
low). 
Ax =Xr"K, +xr"K, + Xr°K, X r°)P +2xyP, (2) 
Ay 2 yr' K, - yr'K, 4 yrK, * 2xyP. - Qy^ 4 Y^P 
where x- x—x, and y 2 y - y, 
rz [52 my. 
x,,y, Principal point coordinates 
Both x,, y, undistorted coordinates have to be corrected by Ax 
and Ay after projecting from ground to image, give distorted 
coordinates and resample the grey value from that position. If 
the distortions of the camera are small the corrections can be 
approximated by using Eq. 2 with opposite signs in the parame- 
ters, but in case of the Mavica, the distortions are large and 
therefore a more accurate computation is needed. In Fig. 7 the 
influence of lens distortions for Mavica FD81 is shown. The 
two-dimensional system of equations (Eq. 3) is solved by calcu- 
lating the Jacobian matrix, inverting and multiplying with the 
observation vector. The solution vector updated and the itera- 
tions continue till the convergence of the solution. As initial ap- 
proximation the undistorted coordinates are used. 
f y) 20 (3) 
f, y) 20 
where 
f,G,y) - x «xr' K,exr! Ke xr'K, (n -2x?)B 
*T2XPP, —x 4, 
f505y) S y * yr K,* yr! K, c yr'K, e (n? 2y?)P, 
*2xyP, - y, * y, 
With x y the undistorted coordinates 
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Figure 6.
	        
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