Architectures for stereo vision

Abstract

Stereo vision is an elementary problem for many computer vision tasks. It has been widely studied under the following two aspects, increasing the quality of the results and accelerating the computational processes. This chapter provides theoretic background on stereo vision systems and discusses architectures and implementations for real-time applications. In particular, the computationally intensive part, the stereo matching, is discussed using one of the leading algorithms, the semi-global matching (SGM) as an example. For this algorithm two implementations are presented in detail on two of the most relevant platforms for real-time image processing today: Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs). Thus, the major differences in designing parallelization techniques for extremely different image processing platforms can be illustrated.