Guehring, Jens 
  
3.3.2 Line Shift Processing 
To meet the formulated requirements, we have developed a new method, called /ine shift processing, to solve the 
correspondence problem fast and precisely. 
As before, efficiency is achieved by making use of the highly parallel nature of our projection unit. However, inherent 
problems in phase shift measurements made us create a new pattern design. We project a sequence of parallel lines, 
achieved by illuminating each n" projector line. For our experiments, we have chosen n —6. 
The evaluation of the so called line shift images is performed similar to the images obtained with a light stripe range 
finder (Section 3.3.3). Six images for the x and six images for the y coordinates are needed to use the whole resolution 
provided by the projector. 
  
  
(b) 
Figure 7. (a) One image of the line shift sequence. (b) Computed range imgage. (c) Rendered view of the obtained 
surface. The black dots are points eliminated by consistency checks, e.g. due to saturated pixels. 
After the line centers have been detected, the gray code sequence is used to resolve ambiguities and determine uniquely 
the projector line number. An oversampling technique, similar to the one used in phase shift processing is used to make 
the ambiguity resolution more robust. 
In the next step we intersect the lines, joining the detected stripe centers, to obtain camera coordinates with sub-pixel 
accuracy for each projector coordinate. 
The transition from camera images to projector images is one of the major differences between the two methods. 
Performing the same steps for an arbitrary number of camera / projector combinations, immediately gives us not only 
the correspondences between image points of a single camera / projector combination but also corresponding points 
between any of the cameras linked by a common projector. 
3.3.3 Locating the Stripe Centers 
A lot of research was done, mainly in the computer vision community, to efficiently determine the center of a light 
stripe with sub-pixel accuracy. (Trucco et al., 1998) compare five major algorithms with respect to bias, introduced by 
the peak detector and evaluate the theoretical and practical behaviour under ideal and under noisy conditions. 
All of the considered algorithms determine the peak position by fitting a 1-D curve to a small neighborhood of the 
maximum of the stripe crossection, assuming a Gaussian intensity profile. 
The algorithms are compared with respect to a bias, introduced by the peak detector and the theoretical and practical 
behavior under ideal and under noisy conditions. 
In summary, the results were that all but some small center of mass filters are reasonably unbiased and that a Gaussian 
approximation, developed by those authors, and two detectors developed by (Blais and Rioux, 1986) performed well 
even under severe noise conditions. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B5. Amsterdam 2000. 333