Fast Neural Network Based X-Ray Tomography of Fruit on a Conveyor Belt
Janssens, E.
Pelt, D.
De Beenhouwer, J.
Van Dael, M.
Verboven, P.
Nicolai, B.
Sijbers, J.
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How to Cite

Janssens E., Pelt D., De Beenhouwer J., Van Dael M., Verboven P., Nicolai B., Sijbers J., 2015, Fast Neural Network Based X-Ray Tomography of Fruit on a Conveyor Belt, Chemical Engineering Transactions, 44, 181-186.
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lnline computed tomography (CT) based food inspection requires a fast image reconstruction method. Filtered back projection (FBP) meets this requirement , but relies on many high quality X-ray radiographs, which are often not available in a conveyor belt acquisition geometry. On the other hand, iterative reconstruction methods may yield high quality images even with a small number of radiographs, but are orders of magnitude slower. Recently, a neural network FBP (NN-FBP) method was proposed for parallel beam data that proved to be fast and lead to high quality images. (Pelt et al. 2013a)In this work, we present an NN-FBP based CT reconstruction method for inline inspection. Using neural networks, the method computes application specific filters for a Hilbert transform FBP (hFBP) based reconstruction. Results from the proposed neural network based hFBP (NN-hFBP) method on fan beam X-ray radiographs of apples show thai, comparedlo conventional reconstruction methods, NN-hFBP generates images of high quality in a short reconstruction time.
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