Possibilistic registration based on unsupervised classification (BMPRUC)

Wissal Ben Marzouka; Basel Solaiman; Atef Hamouda; Zouhour Ben Dhiaf; Khaled Bsaies

doi:10.1117/12.2559923

31 January 2020 Possibilistic registration based on unsupervised classification (BMPRUC)

Wissal Ben Marzouka, Basel Solaiman, Atef Hamouda, Zouhour Ben Dhiaf, Khaled Bsaies

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114333E (2020) https://doi.org/10.1117/12.2559923
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

In this paper, an unsupervised registration approach based on possibility theory, called "Unsupervised Possibilistic registration", is proposed to encounter this problem. It consists on adding an unsupervised projection step that allows matching possibility maps, obtained from the two images instead of the grey-level images (knowing that the thematic classes and their number have no effect on the registration). The experiments and the comparative study using MRI images have shown promising results. It is shown that the proposed unsupervised registration approach overcomes major problems of existing methods and allows temporal complexity optimization.

Citation Download Citation

Wissal Ben Marzouka, Basel Solaiman, Atef Hamouda, Zouhour Ben Dhiaf, and Khaled Bsaies "Possibilistic registration based on unsupervised classification (BMPRUC)", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114333E (31 January 2020); https://doi.org/10.1117/12.2559923

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