The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
A SIFT Descriptor with Global Context
Abstract
Matching points between multiple images of a scene is a vital component of many computer vision tasks. Point matching involves creating a succinct and discriminative descriptor for each point. While current descriptors such as SIFT can find matches between features with unique local neighborhoods, these descriptors typically fail to consider global context to resolve ambiguities that can occur locally when an image has multiple similar regions. This paper presents a feature descriptor that augments SIFT with a global context vector that adds curvilinear shape information from a much larger neighborhood, thus reducing mismatches when multiple local descriptors are similar. It also provides a more robust method for handling 2D nonrigid transformations since points are more effectively matched individually at a global scale rather than constraining multiple matched points to be mapped via a planar homography. We have tested our technique on various images and compare matching accuracy between the SIFT descriptor with global context to that without.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
Feature detection with automatic scale selection
Lindeberg, T. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008045108935 · Cited by 2,541
Local grayvalue invariants for image retrieval
Schmid, C., & Mohr, R. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.589215 · Cited by 1,432
An unbiased detector of curvilinear structures
Steger, C. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.659930 · Cited by 1,392
Zhang, Z., Deriche, R., Faugeras, O., & Luong, Q.-T. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(95)00022-4 · Cited by 1,367
An Affine Invariant Interest Point Detector
Mikolajczyk, K., & Schmid, C. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47969-4_9 · Cited by 1,325
Indexing based on scale invariant interest points
Mikolajczyk, K., & Schmid, C. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937561 · Cited by 1,101
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211478 · Cited by 955
Reliable feature matching across widely separated views
Baumberg, A. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.855899 · Cited by 627
Shape Context: A New Descriptor for Shape Matching and Object Recognition
Belongie, S., Malik, J., & Puzicha, J. (2000) · Cited by 511
A new algorithm for non-rigid point matching
Chui, H., & Rangarajan, A. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.854733 · Cited by 440
Multiscale detection of curvilinear structures in 2-D and 3-D image data
Koller, T. M., Gerig, G., Szekely, G., & Dettwiler, D. (2002) · Proceedings of IEEE International Conference on Computer Vision · doi:10.1109/iccv.1995.466846 · Cited by 314
Pritchett, P., & Zisserman, A. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710802 · Cited by 279
Comparing and evaluating interest points
Schmid, C., Mohr, R., & Bauckhage, C. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710723 · Cited by 265
Graph matching with a dual-step EM algorithm
Cross, A. D. J., & Hancock, E. R. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730557 · Cited by 216
Shape recognition with edge-based features
Mikolajczyk, K., Zisserman, A., & Schmid, C. (2003) · Procedings of the British Machine Vision Conference 2003 · doi:10.5244/c.17.79 · Cited by 195
A geometric criterion for shape-based non-rigid correspondence
Tagare, H. D., O'Shea, D. B., & Rangarajan, A. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466907 · Cited by 41
Cited by
A Review of Point Cloud Registration Algorithms for Mobile Robotics
Pomerleau, F., Colas, F., & Siegwart, R. (2015) · Foundations and Trends® in Robotics · doi:10.1561/2300000035 · Cited by 717
3-D Depth Reconstruction from a Single Still Image
Saxena, A., Chung, S. H., & Ng, A. Y. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0071-y · Cited by 600
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison
Ling, H., & Okada, K. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1058 · Cited by 414
Effective and Efficient Global Context Verification for Image Copy Detection
Zhou, Z., Wang, Y., Wu, Q. M. J., Yang, C.-N., & Sun, X. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2601065 · Cited by 336
Diffusion Distance for Histogram Comparison
Ling, H., & Okada, K. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.99 · Cited by 222
Metadata
- Authors
- Mortensen, E. N., Deng, H., & Shapiro, L.
- Venue
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Publisher
- IEEE
- Published
- 2005-07-27
- Volume
- 1
- Pages
- 184-190
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2131213661
- Cited by
- 256
- References
- 20
- FWCI
- 8.34
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
