The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Robust detection of degenerate configurations while estimating the fundamental matrix
Computer Vision and Image Understanding
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-View Multibody Structure-and-Motion with Outliers through Model Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-View Multibody Structure from Motion
International Journal of Computer Vision
Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
Limits of Motion-Background Segmentation Using Fundamental Matrix Estimation
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Conditions for Segmentation of Motion with Affine Fundamental Matrix
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
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Various computer vision applications involve recovery and estimation of multiple motions from images of dynamic scenes. The exact nature of objects' motions and the camera parameters are often not known a priori and therefore, the most general motion model (the fundamental matrix) is applied. Although the estimation of a fundamental matrix and its use for motion segmentation are well understood, the conditions governing the feasibility of segmentation for different types of motions are yet to be discovered. In this paper, we study the feasibility of separating 2D translations of 3D objects in a dynamic scene. We show that successful segmentation of 2D translations depends on the magnitude of the translations, average distance between the camera and objects, focal length of the camera and level of noise. Extensive set of controlled experiments using both synthetic and real images were conducted to show the validity of the proposed constraints. In addition, we quantified the conditions for successful segmentation of 2D translations in terms of the magnitude of those translations, the average distance between the camera and objects in motions for a given camera. These results are of particular importance for practitioners designing solutions for computer vision problems.