Panahi, Solmaz and Chopin, Jeremy and 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
URI:	Organisation
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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