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Article

Reconstructing Binary Signals from Local Histograms

1
Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark
2
Center for Quantifying Images, MAX IV (QIM), 2800 Kgs. Lyngby, Denmark
*
Author to whom correspondence should be addressed.
Entropy 2022, 24(3), 433; https://doi.org/10.3390/e24030433
Submission received: 25 December 2021 / Revised: 12 March 2022 / Accepted: 15 March 2022 / Published: 21 March 2022
(This article belongs to the Special Issue Application of Entropy to Computer Vision and Medical Imaging)

Abstract

In this paper, we considered the representation power of local overlapping histograms for discrete binary signals. We give an algorithm that is linear in signal size and factorial in window size for producing the set of signals, which share a sequence of densely overlapping histograms, and we state the values for the sizes of the number of unique signals for a given set of histograms, as well as give bounds on the number of metameric classes, where a metameric class is a set of signals larger than one, which has the same set of densely overlapping histograms.
Keywords: local histograms; metameric classes; reconstruction local histograms; metameric classes; reconstruction

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MDPI and ACS Style

Sporring, J.; Darkner, S. Reconstructing Binary Signals from Local Histograms. Entropy 2022, 24, 433. https://doi.org/10.3390/e24030433

AMA Style

Sporring J, Darkner S. Reconstructing Binary Signals from Local Histograms. Entropy. 2022; 24(3):433. https://doi.org/10.3390/e24030433

Chicago/Turabian Style

Sporring, Jon, and Sune Darkner. 2022. "Reconstructing Binary Signals from Local Histograms" Entropy 24, no. 3: 433. https://doi.org/10.3390/e24030433

APA Style

Sporring, J., & Darkner, S. (2022). Reconstructing Binary Signals from Local Histograms. Entropy, 24(3), 433. https://doi.org/10.3390/e24030433

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