Publication
Title
Neural netwok based x-ray tomography for fast inspection of apples on a conveyor belt system
Author
Abstract
The throughput of an inline computed tomography (CT) based inspection system depends on the speed of its image reconstruction algorithm. Filtered back projection (FBP) provides fast reconstructions, but requires many high quality radiographs from all angles to obtain accurate reconstructions. This is not achievable in an inline environment. Iterative reconstruction methods yield adequate reconstructions from limited, but they are slow. Recently a new reconstruction algorithm was introduced [1] that can handle limited data and is very fast: the neural network FBP (NN-FBP). In this work, we introduce a neural network (NN) based Hilbert transform FBP (NN-hFBP) for inline inspection. This method reconstructs images with a filter-based Hilbert transform FBP method. The filters are application specific and trained by a neural network. Comparison of the NN-hFBP and conventional reconstruction methods applied to inline fan-beam X-ray data of apples shows that the NN-hFBP yields high quality images in a short reconstruction time.
Language
English
Source (journal)
Proceedings. - Los Alamitos, Calif, 1994, currens
Source (book)
IEEE International Conference on Image Processing (ICIP), SEP 27-30, 2015, Quebec City, CANADA
Publication
New york : Ieee, 2015
Volume/pages
(2015), p. 917-921
ISI
000371977801006
Number
978-1-4799-8339-1
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 06.06.2016
Last edited 16.06.2017
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