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Title
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Fast neural network based x-ray tomography of fruit on a conveyor belt
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Author
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Abstract
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| Inline 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-hF BP) method on fan beam X-ray radiographs of apples show that, compared to conventional reconstruction methods, NN-hFBP generates images of high quality in a short reconstruction time. |
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Language
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English
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Source (journal)
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FRUTIC ITALY 2015: 9TH NUT AND VEGETABLE PRODUCTION ENGINEERING SYMPOSIUM
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Source (book)
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9th Nut and Vegetable Production Engineering Symposium, MAY 19-22, 2015, Milano, ITALY
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Publication
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Milano
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Aidic servizi srl
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2015
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ISBN
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978-88-95608-35-8
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DOI
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10.3303/CET1544031
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Volume/pages
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44
(2015)
, p. 181-186
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ISI
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000365994700031
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Full text (Publisher's DOI)
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Full text (open access)
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