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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Self-Adaptive Matching In Local
Windows For Depth Estimation |
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Authors: |
Haiqiang Jin, Sheng Liu, Xuhua
Yang, Shengyong Chen |
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Published in:
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(2013).ECMS 2013 Proceedings edited
by: W. Rekdalsbakken, R. T. Bye, H. Zhang European Council for Modeling
and Simulation. doi:10.7148/2013 ISBN:
978-0-9564944-6-7 27th
European Conference on Modelling and Simulation, Aalesund, Norway, May 27th –
30th, 2013 |
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Citation
format: |
Haiqiang Jin, Sheng
Liu, Xuhua Yang, Shengyong
Chen (2013). Self-Adaptive Matching In Local Windows For Depth Estimation, ECMS 2013 Proceedings edited by: W. Rekdalsbakken, R. T. Bye, H. Zhang, European Council for Modeling
and Simulation. doi:10.7148/2013-0831 |
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DOI: |
http://dx.doi.org/10.7148/2013-0831 |
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Abstract: |
This paper proposes a novel
local stereo matching approach based on self-adapting matching window. We improve
the accuracy of stereo matching in 3 steps. First, we integrate shape and
size information, and construct robust minimum matching windows by applying a
self-adapting method. Then, two matching cost optimization strategies are
employed for handling both occlusion regions and image borders. Last, we
perform a refinement algorithm for obtaining more accurate depth map.
Experiment results on the Middlebury stereo image pairs prove that the
proposed matching method performs equally well in comparison with other state-of-the-art
local approaches. |
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