Title
Neural netwok based x-ray tomography for fast inspection of apples on a conveyor belt system Neural netwok based x-ray tomography for fast inspection of apples on a conveyor belt system
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
conferenceObject
Publication
New york :Ieee ,
Subject
Physics
Engineering sciences. Technology
Source (journal)
2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Source (book)
IEEE International Conference on Image Processing (ICIP), SEP 27-30, 2015, Quebec City, CANADA
Volume/pages
(2015) , p. 917-921
ISSN
1522-4880
ISBN
978-1-4799-8339-1
ISI
000371977801006
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
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.
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000371977801006&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000371977801006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
https://repository.uantwerpen.be/docman/iruaauth/01222b/133607.pdf
Handle