Foreign object detection in multispectral X-ray images of food items using sparse discriminant analysis

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2017

DOI

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Non-invasive food inspection and quality assurance are becoming viable techniques in food production due to the introduction of fast and accessible multispectral X-ray scanners. However, the novel devices produce massive amount of data and there is a need for fast and accurate algorithms for processing it. We apply a sparse classifier for foreign object detection and segmentation in multispectral X-ray. Using sparse methods makes it possible to potentially use fewer variables than traditional methods and thereby reduce acquisition time, data volume and classification speed. We report our results on two datasets with foreign objects, one set with spring rolls and one with minced meat. Our results indicate that it is possible to limit the amount of data stored to 50% of the original size without affecting classification accuracy of materials used for training. The method has attractive computational properties, which allows for fast classification of items in new images.
Original languageEnglish
Title of host publicationSCIA 2017
Volume10269
PublisherSpringer
Publication date2017
Pages350-361
ISBN (print)9783319591254
DOIs
StatePublished - 2017
Event20th Scandinavian Conference on Image Analysis - Tromsø, Norway

Conference

Conference20th Scandinavian Conference on Image Analysis
CountryNorway
CityTromsø
Period12/06/201714/06/2017
SeriesLecture Notes in Computer Science
Volume10269
ISSN0302-9743
CitationsWeb of Science® Times Cited: No match on DOI

    Keywords

  • Theoretical Computer Science, Computer Science (all), Foreign object detection, Multispectral, Sparse classification, X-ray, Discriminant analysis, Image analysis, Object recognition, Quality assurance, X rays, Acquisition time, Classification accuracy, Computational properties, Fast and accurate algorithms, Fast classification, Multi-spectral, Sparse classifiers, Object detection
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