The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Reliable feature matching across widely separated views
Abstract
We present a robust method for automatically matching features in images corresponding to the same physical point on an object seen from two arbitrary viewpoints. Unlike conventional stereo matching approaches we assume no prior knowledge about the relative camera positions and orientations. In fact in our application this is the information we wish to determine from the image feature matches. Features are detected in two or more images and characterised using affine texture invariants. The problem of window effects is explicitly addressed by our method-our feature characterisation is invariant to linear transformations of the image data including rotation, stretch and skew. The feature matching process is optimised for a structure-from-motion application where we wish to ignore unreliable matches at the expense of reducing the number of feature matches.
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Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Research works printed but not linked: 17
- kadyrov, 1995, Computer Analysis of Image and Patterns
- 10.1007/978-1-4757-6465-9 · DOI · Paper page
- 10.1016/S0262-8856(97)01144-X · DOI · Paper page
- 10.1007/BF01421488 · doi:10.1007/bf01421488
- 10.1109/ICCV.1998.710802 · DOI · Paper page
- 10.1007/BFb0028347 · doi:10.1007/bfb0028347
- schalkoff, 1989, Digital Image Processing and Computer Vision
- 10.1109/34.589215 · DOI · Paper page
- tomasi, 1994, Good features to track, CVPR94, 593
- faugeras, 1993, Three-Dimensional Computer Vision
- falkenhagen, 1994, Image Processing for Broadcast and Video Production, 115
- 10.1016/0031-3203(93)90098-H · DOI · Paper page
- gruen, 1985, Adaptive least squares correlation: A powerful image matching technique, Jornal of Photogrammetry Remote Sensing and Cartography, 14, 175
- 10.5244/C.12.37 · doi:10.5244/c.12.37
- deriche, 1994, Robust recovery of the epipolar geometry for an uncalibrated stereo rig, ECCV94, a:567
- beardsley, 1996, 3d model acquisition from extended image sequences, ECCV, 2, 683
- xu, 1997, A unified approach to image matching and segmentation in stereo, motion, and object recognition via recovery of epipolar geometry, Videre, 1, 22
Cited by
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
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Metadata
- Authors
- Baumberg, A.
- Venue
- Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Volume
- 1
- Pages
- 774-781
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2165497495
- Cited by
- 627
- References
- 3
- FWCI
- 27.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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