Geometric motion segmentation and model selection

1998 ◽  
Vol 356 (1740) ◽  
pp. 1321-1340 ◽  
Author(s):  
P. H. S. Torr
Keyword(s):  
Model Selection ◽  
Motion Segmentation ◽  
Geometric Motion

scholarly journals Motion Segmentation Based on Model Selection in Permutation Space for RGB Sensors

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2936 ◽  
Author(s):  
Xi Zhao ◽  
Qianqing Qin ◽  
Bin Luo
Keyword(s):  
Model Selection ◽  
Moving Objects ◽  
Motion Segmentation ◽  
Model Fitting ◽  
Selection Strategy ◽  
Camera Model ◽  
Clustering Problem ◽  
Point Trajectories ◽  
Real World Datasets ◽  
Low Dimensional

Motion segmentation is aimed at segmenting the feature point trajectories belonging to independently moving objects. Using the affine camera model, the motion segmentation problem can be viewed as a subspace clustering problem—clustering the data points drawn from a union of low-dimensional subspaces. In this paper, we propose a solution for motion segmentation that uses a multi-model fitting technique. We propose a data grouping method and a model selection strategy for obtaining more distinguishable data point permutation preferences, which significantly improves the clustering. We perform extensive testing on the Hopkins 155 dataset, and two real-world datasets. The experimental results illustrate that the proposed method can deal with incomplete trajectories and the perspective effect, comparing favorably with the current state of the art.

MOTION SEGMENTATION BY SUBSPACE SEPARATION: MODEL SELECTION AND RELIABILITY EVALUATION

2002 ◽  
Vol 02 (02) ◽  
pp. 179-197 ◽  
Author(s):  
KENICHI KANATANI
Keyword(s):  
Model Selection ◽  
Motion Segmentation ◽  
Reliability Evaluation ◽  
Mathematical Theorem ◽  
Robust Segmentation ◽  
Subspace Separation ◽  
Separation Model

Reformulating the Costeira–Kanade algorithm as a pure mathematical theorem, we present a robust segmentation procedure, which we call subspace separation, by incorporating model selection using the geometric AIC. We then study the problem of estimating the number of independent motions using model selection. Finally, we present criteria for evaluating the reliability of individual segmentation results. Again, model selection plays an important role. We confirm the effectiveness of our method by experiments using synthetic and real images.

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