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Abstract: No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose 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... (Update)
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BibTeX entry: (Update)
J. Shi and C. Tomasi. Good features to track. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'94), pages 593--600, IEEE Computer Society, Seattle, Washington, June 1994. http://citeseer.ist.psu.edu/shi94good.html More
@inproceedings{ shi93good,
author = "Jianbo Shi and Carlo Tomasi",
title = "Good Features to Track",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94)",
address = "Seattle",
month = Jun,
year = "1994",
url = "citeseer.ist.psu.edu/shi94good.html" }
Citations (may not include all citations):492 An iterative image registration technique with an applicatio.. - Lucas, Kanade - 1981
333 Good features to track - Shi, Tomasi - 1993 ACM
257 A computational framework and an algorithm for the measureme.. (context) - Anandan - 1989
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31 Algorithms for subpixel registration (context) - Tian, Huhns - 1986 ACM
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8 Motion displacement estimation using an affine model for mat.. (context) - Fuh, Maragos - 1991
8 Photogrammetric Standard Methods and Digital Image Matching .. (context) - Forstner, Pertl - 1986
7 Prediction of correlation errors in stereo-pair images (context) - Ryan, Gray et al. - 1980
4 and early visual information processing (context) - Marr, Poggio et al. - 1979
4 Realities of automatic correlation problem (context) - Wood - 1983
2 Extracting affine deformations from image patches - I: Findi.. - Manmatha, Oliensis - 1993
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