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An automatized method to parameterize embedded stereo matching algorithms
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DOI：10.1016/j.sysarc.2017.09.002.png)
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Abstract
En 中文
Many applications rely on 3D information as a depth map. Stereo Matching algorithms reconstruct a depth map from a pair of stereoscopic images. Stereo Matching algorithms are computationally intensive, that is why implementing efficient stereo matching algorithms on embedded systems is very challenging for real-time applications. Indeed, like many vision algorithms, stereo matching algorithms have to set a lot of parameters and thresholds to work efficiently. When optimizing a stereo-matching algorithm, or changing algorithms parts, all those parameters have to be set manually. Finding the most efficient solution for a stereo matching algorithm on a specific platform then becomes troublesome. This paper proposes an automatized method to find the optimal parameters of a dense stereo matching algorithm by learning from ground truth on a database in order to compare it with respect to any other alternative. Finally, for the C6678 platform, a map of the best compromise between quality and execution time is obtained, with execution times that are between 42 ms and 382 ms and output errors that are between 6% and 9.8%. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Embedded vision
DSP
Stereo matching
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