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- Publisher Website: 10.1109/CSPA.2013.6530012
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Conference Paper: A new high resolution depth map estimation system using stereo vision and depth sensing device
Title | A new high resolution depth map estimation system using stereo vision and depth sensing device |
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Authors | |
Keywords | Depth estimation system High resolution Kinect Stereo vision |
Issue Date | 2013 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002748 |
Citation | The IEEE 9th International Colloquium on Signal Processing and its Applications (CSPA 2013), Kuala Lumpur, Malaysia, 8-10 March 2013. In Conference Proceedings, 2013, p. 49-53 How to Cite? |
Abstract | Depth map estimation is a classical problem in computer vision. Conventional depth estimation relies on stereo/multi-view matching or depth sensing devices alone. In this paper, we propose a system which addresses high resolution and high quality depth estimation based on joint fusion of stereo and Kinect data. The problem is formulated as a maximum a posteriori probability (MAP) estimation problem and reliability of two devices are derived. The depth map estimated is further refined by color image guided depth matting and a 2D polynomial regression (LPR)-based filtering. Experimental results show that our system can provide high quality and resolution depth map, which complements the strengths of stereo vision and Kinect depth sensor. © 2013 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/189921 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Zhang, S | en_US |
dc.contributor.author | Wang, C | en_US |
dc.contributor.author | Chan, SC | en_US |
dc.date.accessioned | 2013-09-17T15:01:18Z | - |
dc.date.available | 2013-09-17T15:01:18Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The IEEE 9th International Colloquium on Signal Processing and its Applications (CSPA 2013), Kuala Lumpur, Malaysia, 8-10 March 2013. In Conference Proceedings, 2013, p. 49-53 | en_US |
dc.identifier.isbn | 978-1-4673-5609-1 | - |
dc.identifier.uri | http://hdl.handle.net/10722/189921 | - |
dc.description.abstract | Depth map estimation is a classical problem in computer vision. Conventional depth estimation relies on stereo/multi-view matching or depth sensing devices alone. In this paper, we propose a system which addresses high resolution and high quality depth estimation based on joint fusion of stereo and Kinect data. The problem is formulated as a maximum a posteriori probability (MAP) estimation problem and reliability of two devices are derived. The depth map estimated is further refined by color image guided depth matting and a 2D polynomial regression (LPR)-based filtering. Experimental results show that our system can provide high quality and resolution depth map, which complements the strengths of stereo vision and Kinect depth sensor. © 2013 IEEE. | - |
dc.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002748 | - |
dc.relation.ispartof | International Colloquium on Signal Processing and Its Applications (CSPA) Proceedings | en_US |
dc.subject | Depth estimation system | - |
dc.subject | High resolution | - |
dc.subject | Kinect | - |
dc.subject | Stereo vision | - |
dc.title | A new high resolution depth map estimation system using stereo vision and depth sensing device | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Zhang, S: szjeff@eee.hku.hk | en_US |
dc.identifier.email | Wang, C: cwang@eee.hku.hk | - |
dc.identifier.email | Chan, SC: ascchan@hkucc.hku.hk | - |
dc.identifier.authority | Chan, SC=rp00094 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CSPA.2013.6530012 | - |
dc.identifier.scopus | eid_2-s2.0-84881062586 | - |
dc.identifier.hkuros | 225080 | en_US |
dc.identifier.hkuros | 225397 | - |
dc.identifier.spage | 49 | en_US |
dc.identifier.epage | 53 | en_US |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 131104 | - |