Abstract
In this paper, the implementation of a new stereo vision process on a specialized architecture which comprises of three DSPs TMS320C31 is described. The first step of our stereo vision system is a self-adaptive image segmentation algorithm based on a new concept that we call declivity. The second step is a new and fast stereo matching algorithm based on dynamic programming and using self-adaptive decision parameters. The goal of our work is to develop a stereo vision system that achieves an acceptable level of performance using a modest amount of hardware. This implementation is organized as follows: declivity extraction from the two stereo images is performed in parallel on two DSPs, one for the right image and the other for the left one. Then, the last DSP computes the declivity matching based on our dynamic programming method as well as the 3D maps calculation. Finally, experimental results obtained using real pairs of stereo images on a VME 150/40 Imaging Technology Vision System are presented. They show the feasibility and the effectiveness of our system. These results can surely be improved by using a new generation of DSP in order to consider real-time applications.
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