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coordinates are also known. By setting up a system of equations that relates these coordinates through
the camera's parameters, one can form a least-squares solution of the parameters [ 23 ] .
Calibration is performed by imaging a set of points whose global coordinates are known and iden-
tifying the image coordinates of the points and the correspondence between the two sets of points. This
results in a series of five-tuples, ( x i , y i , z i , c i , r i ) consisting of the three-dimensional global coordinates and
two-dimensional image coordinates for each point. The two-dimensional image coordinates are a map-
ping of the local two-dimensional image plane of the camera located a distance f in front of the camera
( Eq. B.171 ) . The image plane is located relative to the three-dimensional local coordinate system ( u , v , w )
of the camera ( Figure B.60 ). The imaging of a three-dimensional point is approximated using a pinhole
camera model. The three-dimensional local coordinate system of the camera is related to the three-
dimensional global coordinate system by a rotation and translation ( Eq. B.172 ) ; the origin of the camera's
local coordinate system is assumed to be at the focal point of the camera. The three-dimensional coor-
dinates are related to the two-dimensional coordinates by the transformation to be determined.
Equation B.173 expresses the relationship between a pixel's column and row number and the global coor-
dinates of the point. These equations are rearranged and set equal to zero in Equation B.174 .Theycanbe
put in the form of a system of linear equations ( Eq. B.175 ) so that the unknowns are isolated ( Eq. B.176 )
by using substitutions common in camera calibration ( Eqs. B.176 and B.177 ) . Temporarily dividing
through by t 3 ensures that t 3
0.0 and therefore that the global origin is in front of the camera. This step
results in EquationB.178 , where A 0 is the first 11 columns of A ; B 0 is the last column of A ;and W 0 is the first
11 rows of W. Typically, enough points are captured to ensure an overdetermined system. Then a least-
squares method, such as singular value decomposition, can be used to find the W 0 that satisfies
Equation B.179 . W 0 is related to W by Equation B.180 , and the camera parameters can be recovered
by undoing the substitutions made in Equation B.176 by Equation B.181 . Because of numerical impre-
cision, the rotationmatrix recoveredmay not be orthonormal, so it is best to reconstruct the rotationmatrix
first ( Eq. B.182 ) , massage it into orthonormality, and then use the new rotation matrix to generate the rest
of the parameters ( Eq. B.183 ).
c i c 0 ¼ s u u i
r r 0 ¼ s v v i
(B.171)
2
4
3
5 ¼ R
2
4
3
5 þ T
u i
v i
f
x i
y i
z i
2
4
3
5 ¼
2
4
3
5
r 0 ; 0
r 0 ; 1
r 0 ; 2
R 0
R 1
R 2
R ¼
r 1 ; 0
r 1 ; 1
r 1 ; 2
(B.172)
r 2 ; 0
r 2 ; 1
r 2 ; 2
2
4
3
5
t 0
t 1
t 2
T ¼
u i
f ¼
c i c 0
s u f
c i c 0
f u
R 0 ½x i y i z i þt 0
R 2 ½x i y i z i þt 2
¼
¼
(B.173)
v i
f ¼
r i r 0
s v f
c i c 0
fv
R 1 ½x i y i z i þt 1
R 2 ½x i y i z i þt 2
¼
¼
 
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