The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Fast Keypoint Recognition in Ten Lines of Code
Open accessAbstract
While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
Cited by
Kalal, Z., Mikolajczyk, K., & Matas, J. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.239 · Cited by 3,285
Reading text in the wild with convolutional neural networks
Jaderberg, M., Simonyan, K., Vedaldi, A., & Zisserman, A. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0823-z · Cited by 1,191
Weinmann, M., Jutzi, B., Hinz, S., & Mallet, C. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.01.016 · Cited by 709
Deep Features for Text Spotting
Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10593-2_34 · Cited by 653
Pose tracking from natural features on mobile phones
Wagner, D., Reitmayr, G., Mulloni, A., Drummond, T., & Schmalstieg, D. (2008) · 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2008.4637338 · Cited by 420
Metadata
- Authors
- Ozuysal, M., Fua, P., & Lepetit, V.
- Venue
- 2007 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2007-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2101886560
- Cited by
- 415
- References
- 2
- FWCI
- 30.11
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ozuysal, M., Fua, P., & Lepetit, V. (2007). Fast Keypoint Recognition in Ten Lines of Code. 2007 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2007.383123
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