76 
  
  
  
  
  
histogram 
12000 
10000 
> 
2 8000 
= 
g 6000 streaks 
"7 400 
2000 | 
0 A 
  
0 50 100 (150 20 250 
gray value 
  
Figure 3: Histogram of the left streak image. Although appearing to show a bimodal distribution, particles cannot 
be segmented by a threshold. 
connected object. The image g(x,y) is scanned through for local maxima in the intensity, as the location of 
streaks is well approximated by a local maximum gmax(X,y). A minimum search horizontally and vertically 
from gmax(X,y) enables the calculation of the peak height: 
Ag = min(gmax = Jmin,i) , (1) 
mini being the minima revealed by the minimum search. In addition the half width is measured. Both 
peak height and half width a required to lie above a threshold to prevent random noise being a seeding 
point for the region growing. After these germ points are identified the growing algorithm segments the 
object following two rules: A pixel is accepted as an object point only when its gray value is higher than 
an adaptive threshold, which is calculated from gp, ; by interpolation. Then,only those pixels forming a 
. connected object are considered. A result of the described segmentation algorithm is shown in Fig. 4. Each 
object identified by the segmentation is then labeled with a flood fill algorithm borrowed from computer 
graphics. The size of each object can then be determined, and thereby large objects (reflections at the water 
surface) removed. 
  
Figure 4: Original gray value image left and segmented image right. 501 objects were found. The reflections at the 
water surface were eliminated by the labeling algorithm. 
3.3 Image Sequence Analysis 
After segmentation, the correspondence problem of identifying the same particle in the next image frame 
is solved, by calculating its image field streak overlap: Some cameras (e.g. the Pulnix TM-640) show a 
significant overlap 0 of the exposure in two consecutive fields of the same frame. The overlap of the exposure 
time yields a spatial overlap of the two corresponding streaks from one image to the next. An AND operation 
between two consecutive segmented fields calculates the overlap fast and efficiently [6]. In addition, as the 
temporal order of the image fields is known, the sign of the vector is also known and the directional ambiguity 
is removed. However most cameras do not show a temporal overlap in the exposure time. then, corresponding 
particles will only overlap due to their expansion in space. Artificially this expansion can be increased by 
the use of a morphological dilation operator. The binary dilation operator of the set of object points O by 
a mask M is defined by: 
OoM (p. Mji1OZ, (2) 
IAPRS, Vol. 30, Part 5W1, ISPRS Intercommission Workshop “From Pixels to Sequences”, Zurich, March 22-24 1995