Proceedings ArticleDOI
Making good features track better
T. Tommasini,Andrea Fusiello,Emanuele Trucco,Vito Roberto +3 more
- pp 178-183
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This paper employs a simple and efficient outlier rejection rule, called X84, and proves that its theoretical assumptions are satisfied in the feature tracking scenario, and shows a quantitative example of the benefits introduced by the algorithm for the case of fundamental matrix estimation.Abstract:
This paper addresses robust feature tracking. We extend the well-known Shi-Tomasi-Kanade tracker by introducing an automatic scheme for rejecting spurious features. We employ a simple and efficient outlier rejection rule, called X84, and prove that its theoretical assumptions are satisfied in the feature tracking scenario. Experiments with real and synthetic images confirm that our algorithm makes good features track better; we show a quantitative example of the benefits introduced by the algorithm for the case of fundamental matrix estimation. The complete code of the robust tracker is available via ftp.read more
Citations
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Computer Vision: Algorithms and Applications
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Point matching under large image deformations and illumination changes
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References
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Proceedings Article
An iterative image registration technique with an application to stereo vision
Bruce D. Lucas,Takeo Kanade +1 more
TL;DR: In this paper, the spatial intensity gradient of the images is used to find a good match using a type of Newton-Raphson iteration, which can be generalized to handle rotation, scaling and shearing.
Proceedings ArticleDOI
Good features to track
Jianbo Shi,Tomasi +1 more
TL;DR: A feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world are proposed.
Journal ArticleDOI
Performance of optical flow techniques
TL;DR: These comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques the authors implemented.
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Robust statistics: the approach based on influence functions
TL;DR: This paper presents a meta-modelling framework for estimating the values of Covariance Matrices and Multivariate Location using one-Dimensional and Multidimensional Estimators.
Journal ArticleDOI
Shape and motion from image streams under orthography: a factorization method
Carlo Tomasi,Takeo Kanade +1 more
TL;DR: In this paper, the singular value decomposition (SVDC) technique is used to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively, and two of the three translation components are computed in a preprocessing stage.