Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Invariants of Six Points and Projective Reconstruction From Three Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Duality of Multi-Point and Multi-Frame Geometry: Fundamental Shape Matrices and Tensors
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Duality of reconstruction and positioning from projective views
VSR '95 Proceedings of the IEEE Workshop on Representation of Visual Scenes
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA
International Journal of Computer Vision
Single-view matching constraints
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Multibody Motion Segmentation Using the Geometry of 6 Points in 2D Images
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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We present a method for segmenting an arbitrary number of moving objects in image sequences using the geometry of 6 points in 2D to infer motion consistency. The method has been evaluated on the Hopkins 155 database and surpasses current state-of-the-art methods such as SSC, both in terms of overall performance on two and three motions but also in terms of maximum errors. The method works by finding initial clusters in the spatial domain, and then classifying each remaining point as belonging to the cluster that minimizes a motion consistency score. In contrast to most other motion segmentation methods that are based on an affine camera model, the proposed method is fully projective.