Introduction
In ordinary signal processing, one models physical phenomena as “sources,” which generate “signals” obscured by random “noise.” The sources are to be extracted from the noise using optimal-estimation algorithms. Random set (RS) theory was devised about 40 years ago by mathematicians who also wanted to construct optimal-estimation algorithms. The “signals” and “noise” that they had in mind, however, were geometric patterns in images. The resulting theory, stochastic geometry, is the basis of the “morphological operators” commonly employed today in image-processing applications. It is also the basis for the theory of RSs. An important special case of RS theory, the theory of random finite sets(RFSs), addresses problems in which the patterns of interest consist of a finite number of points. It is the theoretical basis of many modern medical and other image-processing algorithms. In recent years, RFS theory has found application to the problem of detecting, localizing, and...
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Mahler, R. (2013). Estimation for Random Sets. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_70-1
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_70-1
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Latest
Estimation for Random Sets- Published:
- 06 November 2019
DOI: https://doi.org/10.1007/978-1-4471-5102-9_70-2
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Original
Estimation for Random Sets- Published:
- 23 March 2014
DOI: https://doi.org/10.1007/978-1-4471-5102-9_70-1