Stereo matching algorithm using census transform and segment tree for depth estimation
This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. Basically, the proposed framework in this article is developed through a series of functions. The...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Ahmad Dahlan (UAD)
2023
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| Online Access: | http://eprints.utem.edu.my/id/eprint/27100/2/J26-2023-MAIN.PDF http://eprints.utem.edu.my/id/eprint/27100/ http://telkomnika.uad.ac.id/index.php/TELKOMNIKA/article/view/21881 |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. Basically, the proposed framework in this article is developed through a series of
functions. The final result from this framework is disparity map which this map has the information of depth estimation. Fundamentally, the framework input is the stereo image which represents left and right images respectively.
The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. Based on the
experimental results from the standard benchmarking evaluation system from the Middlebury, the disparity map results produce an average low noise error at 9.68% for nonocc error and 18.9% for all error attributes. On average, it performs far better and very competitive with other available
methods from the benchmark system |
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