IEICE TRANSACTIONS on Information
Depth Map Estimation Using Census Transform for Light Field Cameras
Summary :
Depth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.11 pp.2711-2720
- Publication Date
- 2017/11/01
- Publicized
- 2017/08/02
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7052
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Takayuki TOMIOKA
Tottori University
Kazu MISHIBA
Tottori University
Yuji OYAMADA
Tottori University
Katsuya KONDO
Tottori University
Keyword
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Takayuki TOMIOKA, Kazu MISHIBA, Yuji OYAMADA, Katsuya KONDO, "Depth Map Estimation Using Census Transform for Light Field Cameras" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 11, pp. 2711-2720, November 2017, doi: 10.1587/transinf.2017EDP7052.
Abstract: Depth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7052/_p
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@ARTICLE{e100-d_11_2711,
author={Takayuki TOMIOKA, Kazu MISHIBA, Yuji OYAMADA, Katsuya KONDO, },
journal={IEICE TRANSACTIONS on Information},
title={Depth Map Estimation Using Census Transform for Light Field Cameras},
year={2017},
volume={E100-D},
number={11},
pages={2711-2720},
abstract={Depth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.},
keywords={},
doi={10.1587/transinf.2017EDP7052},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Depth Map Estimation Using Census Transform for Light Field Cameras
T2 - IEICE TRANSACTIONS on Information
SP - 2711
EP - 2720
AU - Takayuki TOMIOKA
AU - Kazu MISHIBA
AU - Yuji OYAMADA
AU - Katsuya KONDO
PY - 2017
DO - 10.1587/transinf.2017EDP7052
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2017
AB - Depth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.
ER -
English
