Mr. Venumadhav Vedula Profile

Venumadhav Vedula

at Philips Electronics India Ltd

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Proceedings Article | 24 August 2006 Paper

Registration of large data sets for multimodal inspection

Venumadhav Vedula, George Sheri

Proceedings Volume 6312, 63121C (2006) https://doi.org/10.1117/12.673373

KEYWORDS: Image registration, Nondestructive evaluation, Inspection, Data modeling, Computed tomography, Data fusion, Clouds, Computer aided diagnosis and therapy, Algorithm development, Solid modeling

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Registration plays a key role in multimodal data fusion to extract synergistic information from multiple non-destructive evaluation (NDE) sources. One of the common techniques for registration of point datasets is the Iterative Closest Point (ICP) Algorithm. Generally, modern day NDE techniques generate large datasets and conventional ICP algorithm requires huge amount of time to register datasets to the desired accuracy. In this paper, we present algorithms to aid in the registration of large 3D NDE data sets in less time with the required accuracy. Various methods of coarse registration of data, partial registration and data reduction are used to realize this. These techniques have been used in registration and it is shown that registration can be accomplished to the desired accuracy with more than 90% reduction in time as compared to conventional ICP algorithm. Volumes of interest (VOI) can be defined on the data sets and merged together so that only the features of interest are used in the registration. The proposed algorithm also provides capability for eliminating noise in the data sets. Registration of Computed Tomography (CT) Image data, Coordinate Measuring Machine (CMM) Inspection data and CAD model has been discussed in the present work. The algorithm is generic in nature and can be applied to any other NDE inspection data.

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