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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 |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/15271 |
| 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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