A Principal Component Clustering Approach to Object-Oriented Motion Segmentation and Estimation
Journal of VLSI Signal Processing Systems - Special issue on recent development in video: algorithms, implementation and applications
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
International Journal of Computer Vision
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
3D Facial Feature Extraction and Global Motion Recovery Using Multi-modal Information
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
A Factorization Method Using 3-D Linear Combination for Shape and Motion Recovery
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A Priori and A Posteriori Error Estimates in Recovery of 3D Scenes by Factorization Algorithms
Programming and Computing Software
Shape-From-Silhouette Across Time Part I: Theory and Algorithms
International Journal of Computer Vision
International Journal of Robotics Research
International Journal of Robotics Research
Proceedings of the 2008 symposium on Eye tracking research & applications
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The factorization method, first developed by Tomasi and Kanade, recovers both the shape of an object and its motion from a sequence of images, using many images and tracking many feature points to obtain highly redundant feature position information. The method robustly processes the feature trajectory information using singular value decomposition (SVD), taking advantage of the linear algebraic properties of orthographic projection. However, an orthographic formulation limits the range of motions the method can accommodate. Paraperspective projection, first introduced by Ohta, is a projection model that closely approximates perspective projection by modelling several effects not modelled under orthographic projection, while retaining linear algebraic properties. Our paraperspective factorization method can be applied to a much wider range of motion scenarios, such as image sequences containing significant translational motion toward the camera or across the image. The method also can accommodate missing or uncertain tracking data, which occurs when feature points are occluded or leave the field of view. We present the results of several experiments which illustrate the method''s performance in a wide range of situations, including an aerial image sequence of terrain taken from a low-altitude airplane.