The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
An algorithm for associating the features of two images
Abstract
In this paper we describe an algorithm that operates on the distances between features in the two related images and delivers a set of correspondences between them. The algorithm maximizes the inner product of two matrices, one of which is the desired 'pairing matrix' and the other a 'proximity matrix' with elements exp (-rij2/2 sigma 2), where rij is the distance between two features, one in each image, and sigma is an adjustable scale parameter. The output of the algorithm may be compared with the movements that people perceive when viewing two images in quick succession, and it is found that an increase in sigma affects the computed correspondences in much the same way as an increase in interstimulus interval alters the perceived displacements. Provided that sigma is not too small the algorithm will recover the feature mappings that result from image translation, expansion or shear deformation--transformations of common occurrence in image sequences--even when the displacements of individual features depart slightly from the general trend.
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Citations
Cites
Early processing of visual information
Marr, D. (1976) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1976.0090 · Cited by 1,034
Experimentelle Studien uber das Sehen von Bewegung
Wertheimer, M. (1912) · Cited by 975
Cited by
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
Point Set Registration: Coherent Point Drift
Myronenko, A., & Song, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.46 · Cited by 2,762
A new point matching algorithm for non-rigid registration
Chui, H., & Rangarajan, A. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00009-2 · Cited by 1,560
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Shen, Y., Feng, C., Yang, Y., & Tian, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00478 · Cited by 532
Metadata
- Venue
- Proceedings of the Royal Society of London. Series B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 1991-04-22
- Volume
- 244
- Issue
- 1309
- Pages
- 21-26
- ISSN
- 0080-4649 (ISSN-L)
- 0962-8452
- 1471-2954
- OpenAlex
- W2014425592
- Cited by
- 440
- References
- 2
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- closed
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- An algorithm for associating the features of two images. (1991). Proceedings of the Royal Society of London. Series B: Biological Sciences. https://doi.org/10.1098/rspb.1991.0045
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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