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Proceedings ArticleDOI

Making good features track better

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TLDR
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.

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Book

Computer Vision: Algorithms and Applications

TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
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Digital watermarking-based DCT and JPEG model

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Proceedings ArticleDOI

Online camera pose estimation in partially known and dynamic scenes

TL;DR: A robust approach to real-time camera pose estimation is presented, which does neither depend on offline pre-processing steps nor on pre-knowledge of the entire target scene, and is validated on synthetic as well as on real video sequences.
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Feature tracking for visual servoing purposes

TL;DR: This paper addresses the problem of realizing visual servoing tasks on complex objects in real environments by presenting a set of tracking algorithms that have been used for 10 years to achieve this goal.
Journal ArticleDOI

Point matching under large image deformations and illumination changes

TL;DR: To solve the general point correspondence problem in which the underlying transformation between image patches is represented by a homography, a solution based on extensive use of first order differential techniques is proposed.
References
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Proceedings Article

An iterative image registration technique with an application to stereo vision

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

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

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.