| 1. |
- Cornelius, Hugo, et al.
(author)
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Detecting bilateral symmetry in perspective
- 2006
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In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - 0769526462 - 9780769526461
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Conference paper (peer-reviewed)abstract
- A method is presented for efficiently detecting bilateral symmetry on planar surfaces under perspective projection. The method is able to detect local or global symmetries, locate symmetric surfaces in complex backgrounds, and detect multiple incidences of symmetry. Symmetry is simultaneously evaluated across all locations, scales, orientations and under perspective skew. Feature descriptors robust to local affine distortion are used to match pairs of symmetric features. Feature quadruplets are then formed from these symmetric feature pairs. Each quadruplet hypothesises a locally planar 3D symmetry that can be extracted under perspective distortion. The method is posed independently of a specific feature detector or descriptor. Results are presented demonstrating the efficacy of the method for detecting bilateral symmetry under perspective distortion. Our unoptimised Matlab implementation, running on a standard PC, requires of the order of 20 seconds to process images with 1,000 feature points.
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| 2. |
- Cornelius, Hugo, et al.
(author)
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Detecting rotational symmetry under affine projection
- 2006
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In: 18th International Conference on Pattern Recognition, Vol 2, Proceedings. - LOS ALAMITOS, CA : IEEE COMPUTER SOC. - 0769525210 ; , s. 292-295
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Conference paper (peer-reviewed)abstract
- A new method is presented for detecting planar rotational symmetry under affine projection. The method can deal with partial occlusion and is able to detect multiple rotationally symmetric surfaces in complex backgrounds. The order of rotational symmetry can be estimated, and if it is greater than two, the tilt and orientation of the rotationally symmetric surface can be found and the symmetric region segmented. Local features robust to local affine distortion are matched to obtain pairs of features. Each feature pair hypothesises a set of centres of rotation for different tilts and orientations and the centres of rotation that are close to each other are grouped together to find the dominant rotational symmetries in the image. The method is posed independently of a specific feature detector and descriptor Results are presented on natural images.
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