Full text: XVIIIth Congress (Part B3)

  
  
  
   
  
Another big area of application lies in medical research 
projects. For ergonomic investigations the ability to 
model the spatial movements of feet, ankle and knee 
during the process of walking is of special interest. In 
facial medicine the doctor wants to be able to analyse the 
movements of the upper and lower jaws while the patient 
is chewing. 
That is why it seems useful to connect the advantages of 
high precision spatial measurement of static objects with 
the visualisation of movements as they occur in films. 
2. SET-UP OF THE SYSTEM 
Up to four video cameras (CV-235 monochrome, with a 
resolution of 752x582) and optional two pattern 
projectors are used in this project in order to register a 
dynamical process. The cameras and the projectors are 
connected with a frame-grabber (IC-P with AM-CLR from 
Imaging Technology). During the recording a pattern can 
be projected on to the object (head, knee) so that surface 
descriptions can be generated in a fully automatic way by 
using matching algorithms. Besides, targets are fixed on 
the object in order to provide an easy possibility to detect 
identical anatomical points in different images. 
Over a certain period of time each of the four cameras 
records a sequence of 2D-images (-slices). This 
sequence can be interpreted as one 3D-image (= Space- 
Time-Cube, STC) with the image co-ordinate axes X, y 
and the time-axis T. The STC in figure 1 represents a 
hand which is clenched to a fist and then opened again. 
  
  
  
  
  
  
   
    
  
  
  
   
  
   
   
  
   
    
  
  
  
  
  
  
  
  
  
  
   
  
  
   
  
  
  
  
  
  
  
   
  
  
   
   
  
   
  
  
  
  
  
  
  
  
  
Concerning the axes the special meaning of the T-axis 
should be taken into account. Different to the Z-axis of 
conventional voxel-arrays where all three axes are equal, 
the T-axis has a special meaning because the recorded 
movement of points can only occur in the direction of the 
positive T-axis. 
The four STCs can now be used to generate a motion- 
model with the axes X, Y, Z and T. The slices at different 
moments T = constant can be taken to generate surfaces 
using matching algorithms. All these surfaces should 
take into account the existence of correlation between 
slices adjacent in time. 
For the following computation process first each STC is 
analysed separately. Then the 4D-points of the motion 
model are determined by spatial intersection. 
3. COMPUTATION PROCESS 
When computing the spatial points of the motion-model 
two different methods are being tested. 
3.1 Point Tracking method 
In the first approach one arbitrarily chosen slice is taken 
as initial position. In this slice points of interest are 
determined with the help of feature extraction operators 
(e.g. Fórstner-Operator) [Fórstner, 1987]. To select 
appropriate parameters for the thresholds of the feature 
extraction algorithm in a fullautomatical way, mean value 
and variance of the specified slice are used. 
Next, corresponding points have to be found. This is 
done by tracking each of the feature points through the 
STC. A least-squares-approach [Jáhne, 1993] is used to 
determine the direction to the next corresponding point. 
On the assumption that the direction of the gray value 
gradient. changes in a local 3D-neighbourhood both 
components o1 and o2 of the optical flow can be 
determined (Fig. 2). 
  
  
  
  
  
  
  
4 T-Axis 
«C 
a2 | 2 r 
y-Axis T 
  
  
  
  
  
x-Axis 
  
Fig. 2: Components a1 and o2 describing the local 
direction of a moving object element. The right 
object element is static. 
   
    
   
  
   
  
Fig. 1: The position in the STC of the xT-slice on the left 
is marked by the vertical white line in the last 
recorded image, while the top face of the cube 
shows the yT-slice marked by the horizontal line. 
136 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
    
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