Publication

Stochastic Sampling for Computing the Mutual Information of Two Images

Michaël Unser, Philippe Thévenaz
2003
Conference Papers

Abstract

Mutual information is an attractive registration criterion because it provides a meaningful comparison of images that represent different physical properties. In this paper, we review the shortcomings of three published methods for its computation. We identify the grid effect and the overlap problem as the most severe artifacts that these methods face, and propose a solution based on irregular sampling to solve for the grid effect. By implementing irregular sampling as stochastic sampling, we see that our solution covers the two problems at once, as the overlap problem ceases to be an issue, too.

Official source

https://infoscience.epfl.ch/entities/publication/12be4f43-ae5d-4ad1-8ae0-934e0a57c27e

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Michaël Unser, Philippe Thévenaz
2003
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/12be4f43-ae5d-4ad1-8ae0-934e0a57c27e

About this result

Related concepts (32)

Mutual information

In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the "amount of information" (in units such as shannons (bits), nats or hartleys) obtained about one random variable by observing the other random variable. The concept of mutual information is intimately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the expected "amount of information" held in a random variable.

Information theory

Information theory is the mathematical study of the quantification, storage, and communication of information. The field was originally established by the works of Harry Nyquist and Ralph Hartley, in the 1920s, and Claude Shannon in the 1940s. The field, in applied mathematics, is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering, and electrical engineering. A key measure in information theory is entropy.

Interaction information

The interaction information is a generalization of the mutual information for more than two variables. There are many names for interaction information, including amount of information, information correlation, co-information, and simply mutual information. Interaction information expresses the amount of information (redundancy or synergy) bound up in a set of variables, beyond that which is present in any subset of those variables. Unlike the mutual information, the interaction information can be either positive or negative.

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Stochastic Sampling for Computing the Mutual Information of Two Images

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