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A Key Point Method for Data Registration for MultiSensor Fusion

Zeng, Wenhan, Jiang, Xiang, Lou, Shan and Scott, Paul J. (2016) A Key Point Method for Data Registration for MultiSensor Fusion. In: EUSPEN 16th International Conference & Exhibition, 30th May - 3rd June 2016, Nottingham, UK. (Unpublished)

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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.

Item Type: Conference or Workshop Item (Poster)
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TS Manufactures
Schools: School of Computing and Engineering > Centre for Precision Technologies > EPSRC Centre for Innovative Manufacturing in Advanced Metrology
School of Computing and Engineering
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Depositing User: Wenhan Zeng
Date Deposited: 30 Mar 2017 08:37
Last Modified: 28 Aug 2021 16:07


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