Good features to track Export

@inproceedings{citeulike:1934295, 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 correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments}, author = {Shi, Jianbo and Tomasi, C.}, booktitle = {Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on}, citeulike-article-id = {1934295}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/CVPR.1994.323794}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=323794}, doi = {10.1109/CVPR.1994.323794}, journal = {Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on}, keywords = {affine, features, klt, tracking}, pages = {593--600}, posted-at = {2007-12-04 10:10:29}, priority = {5}, title = {Good features to track}, url = {http://dx.doi.org/10.1109/CVPR.1994.323794}, year = {1994} }

TY - CONF ID - citeulike:1934295 L3 - citeulike-article-id:1934295 N2 - 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 correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments JF - Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on EP - 600 TI - Good features to track T2 - Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on SP - 593 KW - affine KW - features KW - klt KW - tracking AU - Shi, Jianbo AU - Tomasi, C PY - 1994/// UR - http://dx.doi.org/10.1109/CVPR.1994.323794 ER -