Abstract
| Original language | English |
|---|---|
| Title of host publication | SCIA 2017 |
| Volume | 10269 |
| Publisher | Springer |
| Publication date | 2017 |
| Pages | 350-361 |
| ISBN (Print) | 9783319591254 |
| DOIs | |
| Publication status | Published - 2017 |
| Event | 20th Scandinavian Conference on Image Analysis - Tromsø, Norway Duration: 12 Jun 2017 → 14 Jun 2017 |
Conference
| Conference | 20th Scandinavian Conference on Image Analysis |
|---|---|
| Country | Norway |
| City | Tromsø |
| Period | 12/06/2017 → 14/06/2017 |
| Series | Lecture Notes in Computer Science |
|---|---|
| Volume | 10269 |
| ISSN | 0302-9743 |
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
Cite this
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Foreign object detection in multispectral X-ray images of food items using sparse discriminant analysis. / Einarsson, Gudmundur; Jensen, Janus Nørtoft; Paulsen, Rasmus Reinhold; Einarsdottir, Hildur; Ersbøll, Bjarne Kjær; Dahl, Anders Bjorholm; Christensen, Lars Bager.
SCIA 2017. Vol. 10269 Springer, 2017. p. 350-361 (Lecture Notes in Computer Science, Vol. 10269).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Foreign object detection in multispectral X-ray images of food items using sparse discriminant analysis
AU - Einarsson, Gudmundur
AU - Jensen, Janus Nørtoft
AU - Paulsen, Rasmus Reinhold
AU - Einarsdottir, Hildur
AU - Ersbøll, Bjarne Kjær
AU - Dahl, Anders Bjorholm
AU - Christensen, Lars Bager
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Theoretical Computer Science
KW - Computer Science (all)
KW - Foreign object detection
KW - Multispectral
KW - Sparse classification
KW - X-ray
KW - Discriminant analysis
KW - Image analysis
KW - Object recognition
KW - Quality assurance
KW - X rays
KW - Acquisition time
KW - Classification accuracy
KW - Computational properties
KW - Fast and accurate algorithms
KW - Fast classification
KW - Multi-spectral
KW - Sparse classifiers
KW - Object detection
U2 - 10.1007/978-3-319-59126-1_29
DO - 10.1007/978-3-319-59126-1_29
M3 - Article in proceedings
SN - 9783319591254
VL - 10269
T3 - Lecture Notes in Computer Science
SP - 350
EP - 361
BT - SCIA 2017
PB - Springer
ER -