Distinctive Image Features from Scale-Invariant Keypoints Export

@article{citeulike:678563, abstract = {This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.}, address = {Hingham, MA, USA}, author = {Lowe, David G.}, booktitle = {International Journal of Computer Vision}, citeulike-article-id = {678563}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=996342}, citeulike-linkout-1 = {http://dblp.uni-trier.de/rec/bibtex/journals/ijcv/Lowe04}, citeulike-linkout-2 = {http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94}, citeulike-linkout-3 = {http://www.springerlink.com/content/h4l02691327px768}, day = {1}, doi = {10.1023/B:VISI.0000029664.99615.94}, issn = {0920-5691}, journal = {International Journal of Computer Vision}, keywords = {c-bag-of-features, s-read, t-imaging}, month = {November}, number = {2}, pages = {91--110}, posted-at = {2009-04-21 20:42:36}, priority = {5}, publisher = {Kluwer Academic Publishers}, title = {Distinctive Image Features from Scale-Invariant Keypoints}, url = {http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94}, volume = {60}, year = {2004} }

TY - JOUR ID - citeulike:678563 L3 - citeulike-article-id:678563 N2 - This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance. IS - 2 JF - International Journal of Computer Vision SN - 0920-5691 AD - Hingham, MA, USA EP - 110 TI - Distinctive Image Features from Scale-Invariant Keypoints PB - Kluwer Academic Publishers VL - 60 T2 - International Journal of Computer Vision SP - 91 KW - c-bag-of-features KW - s-read KW - t-imaging AU - Lowe, David PY - 2004/11/01/ UR - http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94 ER -