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Panahi, Solmaz, Chopin, Jeremy, Ulicny, Matej and Dahyot, Rozenn (2023) Improving GMM registration with class encoding. In: Irish Machine Vision and Image Processing Conference 2023, 30th August 2023, University of Galway.
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Abstract
Point set registration is critical in many applications such as computer vision, pattern recognition, or in
fields like robotics and medical imaging. This paper focuses on reformulating point set registration using
Gaussian Mixture Models while considering attributes associated with each point. Our approach introduces
class score vectors as additional features to the spatial data information. By incorporating these attributes,
we enhance the optimization process by penalizing incorrect matching terms. Experimental results show
that our approach with class scores outperforms the original algorithm by [Jian and Vemuri, 2011] in both
accuracy and speed.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | Point set registration; Graph matching; Gaussian mixture models; GMMs; |
| Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 17751 |
| Depositing User: | Solmaz Panahi |
| Date Deposited: | 25 Oct 2023 15:10 |
| Journal or Publication Title: | Irish Machine Vision and Image Processing Conference 2023 |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/17751 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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