Only few problems in computer vision have been investigated more vigorously than 
stereo. Nevertheless, the main obstacle on the way to their practical 
application is the excessively long computation time needed to match stereo 
images. This paper presents parallel algorithms for edge-based stereo suitable 
for depth computation. Edge-based stereo techniques produce only sparse depth 
maps. Thus, we present in addition an efficient parallel algorithm for dense 
stereo matching that can be employed in scene reconstruction. Both approaches 
are implemented on several different computers to measure the performance. We 
compared single processor and multiple processor implementations to evaluate the 
profit of parallel realizations. Results are presented in this paper. We show 
that both approaches are very suitable for parallel implementations and that 
computing time can be considerably reduced with parallel implementations. 
Furthermore, we present the results that are obtained when employing the 
different approaches to stereo images.