A J-linkage based approach for vanishing direction estimation in catadioptric images

Duan, Wenting and Allinson, Nigel (2014) A J-linkage based approach for vanishing direction estimation in catadioptric images. In: 22nd International Conference on Pattern Recognition (ICPR), 24-18 August 2014, Stockholm, Sweden.

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A J-linkage based approach for vanishing direction estimation in catadioptric images

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Item Type:Conference or Workshop contribution (Paper)
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This paper presents a new method for the extraction and clustering of Catadioptric Line Images (CLIs) from a single catadioptric image. The estimation of vanishing directions is also achieved simultaneously. With the mirror parameters provided, the central catadioptric system can be fully calibrated using the projected mirror boundary. Once the system is fully calibrated, the problem of conic fitting is reduced from five parameters to two parameters. This method requires the central catadioptric system to be vertically aligned i.e. the mirror axis is orthogonal to the floor. In a man-made environment where lots of parallel and orthogonal lines are presented, placing the catadioptric system in such a way allows us to obtain some degenerate CLIs, e.g. projections of lines orthogonal or parallel to the mirror axis. In this paper, we integrate the geometric properties associated with the degenerate CLIs into a J-linkage based algorithm for CLIs clustering and Vanishing Points (VPs) estimation. An exhaustive analysis on the influence of arc occlusion and noise to CLI fitting and grouping is also performed. The proposed approach is targeted to tackle the problem of occlusion and noise. The evaluation of the method is carried out on both synthetic and real data.

Keywords:Calibration, Image processing, Imaging systems, Mirrors, Pattern recognition, Telescopes, Central catadioptric cameras, Central catadioptric systems, Clustering, Conic fitting, Direction estimation, Geometric properties, Synthetic and real data, Vanishing point, Clustering algorithms
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:16528
Deposited On:28 Jan 2015 14:55

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