A key point method for data registration for MultiSensor fusion

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It has been recognized that multi-sensor data fusion can provide a more holistic, accurate and reliable information of the measured surface. Data registration, which is used to align data into one coordinate system, is a key step of data fusion. Widely used feature-based methods find correspondence between features, and then a geometrical transformation is determined to map the target data to the reference data. Reliable and accurate feature selection is thus very important for data registration. In this research, a reliable key point method called Scale Invariant Feature Method (SIFM) for data registration is investigated. By using this method, for each data, one can build a set of feature descriptors of the defined key points, which have the scale/shift/rotation invariant properties. Then the correspondence of two data and geometrical transformation can be achieved by finding the matching of two feature descriptors through closeness measurement. Initial tests on freeform and structured surfaces have proven the effectiveness and efficiency of the method.

Original languageEnglish
Title of host publicationProceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016
Publishereuspen
Pages69-70
Number of pages2
ISBN (Electronic)9780956679086
Publication statusPublished - 2016
Event16th International Conference of the European Society for Precision Engineering and Nanotechnology - East Midlands Conference Centre, Nottingham, United Kingdom
Duration: 30 May 20163 Jun 2016
Conference number: 16
https://www.euspen.eu/events/16th-international-conference-exhibition/ (Link to Conference Website)

Conference

Conference16th International Conference of the European Society for Precision Engineering and Nanotechnology
Abbreviated titleEUSPEN 2016
CountryUnited Kingdom
CityNottingham
Period30/05/163/06/16
OtherThis event offers the possibility to see latest advances in traditional precision engineering fields such as metrology, ultra precision machining, additive and replication processes, precision mechatronic systems & control and precision cutting processes.
Internet address

Fingerprint

Sensor data fusion
multisensor fusion
Data fusion
Feature extraction
shift
sensors

Cite this

Zeng, W., Jiang, X., Lou, S., & Scott, P. (2016). A key point method for data registration for MultiSensor fusion. In Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016 (pp. 69-70). euspen.
Zeng, Wenhan ; Jiang, Xiangqian ; Lou, Shan ; Scott, Paul. / A key point method for data registration for MultiSensor fusion. Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016. euspen, 2016. pp. 69-70
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title = "A key point method for data registration for MultiSensor fusion",
abstract = "It has been recognized that multi-sensor data fusion can provide a more holistic, accurate and reliable information of the measured surface. Data registration, which is used to align data into one coordinate system, is a key step of data fusion. Widely used feature-based methods find correspondence between features, and then a geometrical transformation is determined to map the target data to the reference data. Reliable and accurate feature selection is thus very important for data registration. In this research, a reliable key point method called Scale Invariant Feature Method (SIFM) for data registration is investigated. By using this method, for each data, one can build a set of feature descriptors of the defined key points, which have the scale/shift/rotation invariant properties. Then the correspondence of two data and geometrical transformation can be achieved by finding the matching of two feature descriptors through closeness measurement. Initial tests on freeform and structured surfaces have proven the effectiveness and efficiency of the method.",
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author = "Wenhan Zeng and Xiangqian Jiang and Shan Lou and Paul Scott",
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Zeng, W, Jiang, X, Lou, S & Scott, P 2016, A key point method for data registration for MultiSensor fusion. in Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016. euspen, pp. 69-70, 16th International Conference of the European Society for Precision Engineering and Nanotechnology, Nottingham, United Kingdom, 30/05/16.

A key point method for data registration for MultiSensor fusion. / Zeng, Wenhan; Jiang, Xiangqian; Lou, Shan; Scott, Paul.

Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016. euspen, 2016. p. 69-70.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A key point method for data registration for MultiSensor fusion

AU - Zeng, Wenhan

AU - Jiang, Xiangqian

AU - Lou, Shan

AU - Scott, Paul

PY - 2016

Y1 - 2016

N2 - It has been recognized that multi-sensor data fusion can provide a more holistic, accurate and reliable information of the measured surface. Data registration, which is used to align data into one coordinate system, is a key step of data fusion. Widely used feature-based methods find correspondence between features, and then a geometrical transformation is determined to map the target data to the reference data. Reliable and accurate feature selection is thus very important for data registration. In this research, a reliable key point method called Scale Invariant Feature Method (SIFM) for data registration is investigated. By using this method, for each data, one can build a set of feature descriptors of the defined key points, which have the scale/shift/rotation invariant properties. Then the correspondence of two data and geometrical transformation can be achieved by finding the matching of two feature descriptors through closeness measurement. Initial tests on freeform and structured surfaces have proven the effectiveness and efficiency of the method.

AB - It has been recognized that multi-sensor data fusion can provide a more holistic, accurate and reliable information of the measured surface. Data registration, which is used to align data into one coordinate system, is a key step of data fusion. Widely used feature-based methods find correspondence between features, and then a geometrical transformation is determined to map the target data to the reference data. Reliable and accurate feature selection is thus very important for data registration. In this research, a reliable key point method called Scale Invariant Feature Method (SIFM) for data registration is investigated. By using this method, for each data, one can build a set of feature descriptors of the defined key points, which have the scale/shift/rotation invariant properties. Then the correspondence of two data and geometrical transformation can be achieved by finding the matching of two feature descriptors through closeness measurement. Initial tests on freeform and structured surfaces have proven the effectiveness and efficiency of the method.

KW - Data registration

KW - Multi sensor data fusion

KW - Scale invariant key point

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Zeng W, Jiang X, Lou S, Scott P. A key point method for data registration for MultiSensor fusion. In Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016. euspen. 2016. p. 69-70