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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2018.2827049
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|a eng
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|a Zhu, Kang
|e verfasserin
|4 aut
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|a Hyperspectral Light Field Stereo Matching
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|c 2019
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|a Date Revised 20.11.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a 5 ×6 array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses
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|a Journal Article
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|a Xue, Yujia
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fu, Qiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kang, Sing Bing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Xilin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Jingyi
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 41(2019), 5 vom: 20. Mai, Seite 1131-1143
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|x 1939-3539
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|g volume:41
|g year:2019
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|g day:20
|g month:05
|g pages:1131-1143
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|u http://dx.doi.org/10.1109/TPAMI.2018.2827049
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