ration 
6.3.2. 
frame 
the 
tion 
search 
esults 
d on 
first 
pixels 
on of 
indow 
indow 
  
ght 
8 
5 
6 
0 
4 
0 
4 
4 
7 
'9 
6 
4 
  
  
bility 
along 
za. If 
t the 
one) 
ursive 
  
  
  
  
  
  
procedure this value is reduced to 152,259 pixels. If 
instead of a window search, the whole image were used, 
1,000,000 pixels should be analyzed. This simple 
simulation shows a reduction of 80% in the number of 
pixels to be analyzed; it is difficult to foresee the 
reduction in terms of floating point operations because 
only part of those pixels belong to edges and 
contribute to accumulation in Hough space. 
7. CONCLUSIONS 
We have presented a recursive approach for camera 
calibration and object location based on straight lines 
correspondences and state estimation using Kalman 
Filtering. 
The derivation was presented of a explicit 
funtional model which relates image and object straight 
lines. The Iterated Extended Kalman Filter was 
introduced and applied to the functional model aiming 
the estimation of camera to object transformation. 
An iterative procedure was introduced for 
reduction of the search space in feature extraction 
level. It has been shown that this procedure enables a 
great optimization in time processing. 
The proposed approach was tested using simulated 
data and feature extraction with sub-pixel accuracy. 
Results for single and multi-frame calibration were 
presented. The single frame calibration was used to 
show the filter convergence and the iterative 
reduction of the feature search windows, whereas the 
multi-frame calibration wasused to show the filter 
convergence over several frames taken for different 
cube positions. It was shown that small noise in the 
predicted state vector does not affect the filter 
convergence. 
Although a simple dynamic model of linear motion 
was used, it is expected that similar results arise for 
more complex models. 
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