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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ji-Ho Cho Humenberger, M. |
| Copyright Year | 2015 |
| Description | Author affiliation: Inst. of Software Technol. & Interactive Syst., Vienna Univ. of Technol., Vienna, Austria (Ji-Ho Cho) || AIT Austrian Inst. of Technol., Vienna, Austria (Humenberger, M.) |
| Abstract | Due to recent developments of low-cost image sensors and high-performance embedded processing hardware, future cars and automotive systems will increasingly use binocular stereo vision for environmental perception. However, research and development in stereo vision is still ongoing since there are many challenges unsolved. In this paper, we propose a fast and accurate stereo matching algorithm, designed for automotive applications. It convincingly handles real-world scenes containing complex, textureless, and slanted surfaces. To achieve that, we propose an improved PatchMatch stereo algorithm that combines a census-based cost function with Semi-Global Matching optimization integrated in a cross-scale fusion processing scheme. To further accelerate the algorithm, we propose a novel enhancement approach for PatchMatch-based approximation which allows us to skip the random search or at least significantly reduce the number of iterations. Our method is ranked in the upper third of the KITTI benchmark and among the top performers in terms of processing time. |
| Starting Page | 802 |
| Ending Page | 807 |
| File Size | 768999 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781467372664 |
| DOI | 10.1109/IVS.2015.7225783 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-28 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Approximation algorithms Approximation methods Graphics processing units Stereo vision Cost function Automotive applications |
| Content Type | Text |
| Resource Type | Article |
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