Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Intelligent filtering by semantic importance for single-view 3d reconstruction from snooker video
AU - Legg, Philip A.
AU - Parry, Matthew L.
AU - Chung, David H. S.
AU - Jiang, Richard M.
AU - Morris, Adrian
AU - Griffiths, Iwan W.
AU - Marshall, David
AU - Chen, Min
PY - 2011
Y1 - 2011
N2 - In this paper we investigate the challenge of 3D reconstruction from Snooker video data. We propose a system pipeline for intelligent filtering based on semantic importance in Snooker. The system can be divided into table detection and correction, followed by ball detection, classification and tracking. It is apparent from previous work that there are several challenges presented here. Firstly, previous methods tend to use a fixed top-down camera mounted above the table. To capture a full table view from this is challenging due to space limitations above the table. Instead, we capture video data from a tripod and correct the viewpoint through processing. Secondly, previous methods tend to simply detect the balls without considering other interfering objects such as player and cue. This becomes even more apparent when the player strikes the cue ball. Our intelligent filtering avoids such issues to give accurate 3D table reconstruction.
AB - In this paper we investigate the challenge of 3D reconstruction from Snooker video data. We propose a system pipeline for intelligent filtering based on semantic importance in Snooker. The system can be divided into table detection and correction, followed by ball detection, classification and tracking. It is apparent from previous work that there are several challenges presented here. Firstly, previous methods tend to use a fixed top-down camera mounted above the table. To capture a full table view from this is challenging due to space limitations above the table. Instead, we capture video data from a tripod and correct the viewpoint through processing. Secondly, previous methods tend to simply detect the balls without considering other interfering objects such as player and cue. This becomes even more apparent when the player strikes the cue ball. Our intelligent filtering avoids such issues to give accurate 3D table reconstruction.
KW - image processing
KW - image analysis
KW - object recognition
KW - morphological operations
M3 - Conference contribution/Paper
SN - 9781457713040
T3 - 2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
SP - 2385
EP - 2388
BT - 2011 18th IEEE International Conference on Image Processing
PB - IEEE
ER -