The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Alignment by maximization of mutual information
Open accessAbstract
A new information-theoretic approach is presented for finding the pose of an object in an image. The technique does not require information about the surface properties of the object, besides its shape, and is robust with respect to variations of illumination. In our derivation, few assumptions are made about the nature of the imaging process. As a result, the algorithms are quite general and can foreseeably be used in a wide variety of imaging situations. Experiments are presented that demonstrate the approach in registering magnetic resonance images, aligning a complex 3D object model to real scenes including clutter and occlusion, tracking a human head in a video sequence and aligning a view-based 2D object model to real images. The method is based on a formulation of the mutual information between the model and the image. As applied in this paper, the technique is intensity-based, rather than feature-based. It works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust then traditional correlation. Additionally, it has an efficient implementation that is based on stochastic approximation.>
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
WIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
Cited by
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265
Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547
Image matching as a diffusion process: an analogy with maxwell’s demons
Thirion, J.-P. (1998) · Medical Image Analysis · doi:10.1016/s1361-8415(98)80022-4 · Cited by 2,445
Sequence-independent segmentation of magnetic resonance images
Fischl, B., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.016 · Cited by 2,109
A comparison of similarity measures for use in 2-D-3-D medical image registration
Penney, G. P., Weese, J., Little, J. A., Desmedt, P., Hill, D. L. G., & hawkes, D. J. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.730403 · Cited by 782
Metadata
- Authors
- Viola, P., & Wells, W. M.
- Venue
- Proceedings of IEEE International Conference on Computer Vision
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-19
- Pages
- 16-23
- OpenAlex
- W2140935196
- Cited by
- 1,082
- References
- 3
- FWCI
- 50.27
- Type
- conference paper
- Language
- en
- Open access
- green
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- Viola, P., & Wells, W. M. (2002). Alignment by maximization of mutual information. Proceedings of IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.1995.466930
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