Conference paper · Book chapter
Foreign object detection in multispectral X-ray images of food items using sparse discriminant analysis
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Copenhagen Center for Health Technology, Centers, Technical University of Denmark4
Danish Technological Institute5
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.
Language: | English |
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Publisher: | Springer |
Year: | 2017 |
Pages: | 350-361 |
Proceedings: | 20th Scandinavian Conference on Image Analysis |
Series: | Lecture Notes in Computer Science |
Journal subtitle: | 20th Scandinavian Conference, Scia 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I |
ISBN: | 3319591258 , 3319591266 , 9783319591254 and 9783319591261 |
ISSN: | 03029743 |
Types: | Conference paper and Book chapter |
DOI: | 10.1007/978-3-319-59126-1_29 |
ORCIDs: | Einarsson, Gudmundur , Jensen, Janus Nørtoft , Paulsen, Rasmus Reinhold , Ersbøll, Bjarne Kjær and Dahl, Anders Bjorholm |
Acquisition time Classification accuracy Computational properties Computer Science (all) Discriminant analysis Fast and accurate algorithms Fast classification Foreign object detection Image analysis Multi-spectral Multispectral Object detection Object recognition Quality assurance SDG 2 - Zero Hunger Sparse classification Sparse classifiers Theoretical Computer Science X rays X-ray