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Title: Perspective motion segmentation via collaborative clustering
Authors: Li, Z.
Guo, J.
Cheong, L.-F. 
Zhou, S.Z. 
Issue Date: 2013
Citation: Li, Z., Guo, J., Cheong, L.-F., Zhou, S.Z. (2013). Perspective motion segmentation via collaborative clustering. Proceedings of the IEEE International Conference on Computer Vision : 1369-1376. ScholarBank@NUS Repository.
Abstract: This paper addresses real-world challenges in the motion segmentation problem, including perspective effects, missing data, and unknown number of motions. It first formulates the 3-D motion segmentation from two perspective views as a subspace clustering problem, utilizing the epipolar constraint of an image pair. It then combines the point correspondence information across multiple image frames via a collaborative clustering step, in which tight integration is achieved via a mixed norm optimization scheme. For model selection, we propose an over-segment and merge approach, where the merging step is based on the property of the ell-1-norm of the mutual sparse representation of two over-segmented groups. The resulting algorithm can deal with incomplete trajectories and perspective effects substantially better than state-of-the-art two-frame and multi-frame methods. Experiments on a 62-clip dataset show the significant superiority of the proposed idea in both segmentation accuracy and model selection. © 2013 IEEE.
Source Title: Proceedings of the IEEE International Conference on Computer Vision
ISBN: 9781479928392
DOI: 10.1109/ICCV.2013.173
Appears in Collections:Staff Publications

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