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Alignment by Maximization of Mutual Information
Abstract
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 may foreseeably be used in a wide variety of imaging situations.Experiments are presented that demonstrate the approach registering magnetic resonance (MR) 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 here 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 than traditional correlation. Additionally, it has an efficient implementation that is based on stochastic approximation.
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“Using mutual information in the field of computer vision, first proposed by (Viola and Wells III 1997; Maes et al. 1997) and has been employed as a robust method for comparing image similarity (Klein, Staring, and Pluim 2007; Kothandaraman et al. 2023) by binning pixel values into histograms and…”
Method
Metadata
- Authors
- Viola, P., & Wells III, W. M.
- Venue
- International Journal of Computer Vision
- Published
- 1997-09
- Volume
- 24
- Issue
- 2
- Pages
- 137-154
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1874027545
- Cited by
- 2,610
- References
- 1
- FWCI
- 32.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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