Arellano, Claudia and Dahyot, Rozenn (2012) Mean shift algorithm for robust rigid registration between Gaussian Mixture Models. 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO). ISSN 2076-1465

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Abstract

We present a Mean shift (MS) algorithm for solving the rigid point set transformation estimation [1]. Our registration algorithm minimises exactly the Euclidean distance between Gaussian Mixture Models (GMMs). We show experimentally that our algorithm is more robust than previous implementations [1], thanks to both using an annealing framework (to avoid local extrema) and using variable bandwidths in our density estimates. Our approach is applied to 3D real data sets captured with a Lidar scanner and Kinect sensor.

Item Type:	Article
Keywords:	Mean Shift; Registration; Gaussian Mixture Models; Rigid Transformation;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15271
Depositing User:	Rozenn Dahyot
Date Deposited:	18 Jan 2022 16:42
Journal or Publication Title:	2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
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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