A robust point matching algorithm for non-rigid registration using the Cauchy-Schwarz divergence

Erion Hasanbelliu; Luis Sanchez Giraldo; José C. Príncipe

doi:10.1109/MLSP.2011.6064593

A robust point matching algorithm for non-rigid registration using the Cauchy-Schwarz divergence

Erion Hasanbelliu, Luis Sanchez Giraldo, José C. Príncipe

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

In this paper, we describe an algorithm that provides both rigid and non-rigid point-set registration. The point sets are represented as probability density functions and the registration problem is treated as distribution alignment. Using the PDFs instead of the points provides a more robust way of dealing with outliers and noise, and it mitigates the need to establish a correspondence between the points in the two sets. The algorithm operates on the distance between the two PDFs to recover the spatial transformation function needed to register the two point sets. The distance measure used is the Cauchy-Schwarz divergence. The algorithm is robust to noise and outliers, and performswell in varying degrees of transformations and noise.

Original language	English
Title of host publication	2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
DOIs	https://doi.org/10.1109/MLSP.2011.6064593
State	Published - 2011
Event	21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China Duration: Sep 18 2011 → Sep 21 2011

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing

Conference

Conference	21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
Country/Territory	China
City	Beijing
Period	9/18/11 → 9/21/11

Keywords

Cauchy-Schwarz divergence
information theoretic learning
non-rigid registration
shape matching

ASJC Scopus subject areas

Human-Computer Interaction
Signal Processing

Access to Document

10.1109/MLSP.2011.6064593

Cite this

Hasanbelliu, E., Giraldo, L. S., & Príncipe, J. C. (2011). A robust point matching algorithm for non-rigid registration using the Cauchy-Schwarz divergence. In 2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011 Article 6064593 (IEEE International Workshop on Machine Learning for Signal Processing). https://doi.org/10.1109/MLSP.2011.6064593

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abstract = "In this paper, we describe an algorithm that provides both rigid and non-rigid point-set registration. The point sets are represented as probability density functions and the registration problem is treated as distribution alignment. Using the PDFs instead of the points provides a more robust way of dealing with outliers and noise, and it mitigates the need to establish a correspondence between the points in the two sets. The algorithm operates on the distance between the two PDFs to recover the spatial transformation function needed to register the two point sets. The distance measure used is the Cauchy-Schwarz divergence. The algorithm is robust to noise and outliers, and performswell in varying degrees of transformations and noise.",

keywords = "Cauchy-Schwarz divergence, information theoretic learning, non-rigid registration, shape matching",

author = "Erion Hasanbelliu and Giraldo, {Luis Sanchez} and Pr{\'i}ncipe, {Jos{\'e} C.}",

year = "2011",

doi = "10.1109/MLSP.2011.6064593",

language = "English",

isbn = "9781457716232",

series = "IEEE International Workshop on Machine Learning for Signal Processing",

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note = "21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 ; Conference date: 18-09-2011 Through 21-09-2011",

}

Hasanbelliu, E, Giraldo, LS & Príncipe, JC 2011, A robust point matching algorithm for non-rigid registration using the Cauchy-Schwarz divergence. in 2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011., 6064593, IEEE International Workshop on Machine Learning for Signal Processing, 21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011, Beijing, China, 9/18/11. https://doi.org/10.1109/MLSP.2011.6064593

A robust point matching algorithm for non-rigid registration using the Cauchy-Schwarz divergence. / Hasanbelliu, Erion; Giraldo, Luis Sanchez; Príncipe, José C.
2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011. 2011. 6064593 (IEEE International Workshop on Machine Learning for Signal Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In this paper, we describe an algorithm that provides both rigid and non-rigid point-set registration. The point sets are represented as probability density functions and the registration problem is treated as distribution alignment. Using the PDFs instead of the points provides a more robust way of dealing with outliers and noise, and it mitigates the need to establish a correspondence between the points in the two sets. The algorithm operates on the distance between the two PDFs to recover the spatial transformation function needed to register the two point sets. The distance measure used is the Cauchy-Schwarz divergence. The algorithm is robust to noise and outliers, and performswell in varying degrees of transformations and noise.

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A robust point matching algorithm for non-rigid registration using the Cauchy-Schwarz divergence

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Keywords

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