Update cvmlmu.bib

Author: Roznn

cvmlmu.bib

@@ -8,7 +8,7 @@ @inproceedings{Panahi2023
 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  in both accuracy and speed.},
+with class scores outperforms the original algorithm  in both accuracy and speed. },
 url= {https://github.com/solmak97/GMMReg_Extension},
 doi={},
 note={},