Novelty detection of foreign objects in food using multi-modal X-ray imaging
(2016) In Food Control 67. p.39-47- Abstract
In this paper we demonstrate a method for novelty detection of foreign objects in food products using grating-based multimodal X-ray imaging. With this imaging technique three modalities are available with pixel correspondence, enhancing organic materials such as wood chips, insects and soft plastics not detectable by conventional X-ray absorption radiography. We conduct experiments, where several food products are imaged with common foreign objects typically found in the food processing industry. To evaluate the benefit from using this multi-contrast X-ray technique over conventional X-ray absorption imaging, a novelty detection scheme based on well known image- and statistical analysis techniques is proposed. The results show that the... (More)
In this paper we demonstrate a method for novelty detection of foreign objects in food products using grating-based multimodal X-ray imaging. With this imaging technique three modalities are available with pixel correspondence, enhancing organic materials such as wood chips, insects and soft plastics not detectable by conventional X-ray absorption radiography. We conduct experiments, where several food products are imaged with common foreign objects typically found in the food processing industry. To evaluate the benefit from using this multi-contrast X-ray technique over conventional X-ray absorption imaging, a novelty detection scheme based on well known image- and statistical analysis techniques is proposed. The results show that the presented method gives superior recognition results and highlights the advantage of grating-based imaging.
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- author
- Einarsdóttir, Hildur ; Emerson, Monica Jane ; Clemmensen, Line Harder ; Scherer, Kai ; Willer, Konstantin ; Bech, Martin LU ; Larsen, Rasmus ; Ersbøll, Bjarne Kjær and Pfeiffer, Franz
- organization
- publishing date
- 2016-09-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Dark-field imaging, Foreign object detection, Novelty detection, Phase-contrast imaging, Texture analysis, X-ray radiography
- in
- Food Control
- volume
- 67
- pages
- 9 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:84959333129
- wos:000375163800006
- ISSN
- 0956-7135
- DOI
- 10.1016/j.foodcont.2016.02.023
- language
- English
- LU publication?
- yes
- id
- 70884aeb-0600-4d29-9dc3-286ac8541104
- date added to LUP
- 2016-04-11 10:54:08
- date last changed
- 2024-08-23 09:29:25
@article{70884aeb-0600-4d29-9dc3-286ac8541104, abstract = {{<p>In this paper we demonstrate a method for novelty detection of foreign objects in food products using grating-based multimodal X-ray imaging. With this imaging technique three modalities are available with pixel correspondence, enhancing organic materials such as wood chips, insects and soft plastics not detectable by conventional X-ray absorption radiography. We conduct experiments, where several food products are imaged with common foreign objects typically found in the food processing industry. To evaluate the benefit from using this multi-contrast X-ray technique over conventional X-ray absorption imaging, a novelty detection scheme based on well known image- and statistical analysis techniques is proposed. The results show that the presented method gives superior recognition results and highlights the advantage of grating-based imaging.</p>}}, author = {{Einarsdóttir, Hildur and Emerson, Monica Jane and Clemmensen, Line Harder and Scherer, Kai and Willer, Konstantin and Bech, Martin and Larsen, Rasmus and Ersbøll, Bjarne Kjær and Pfeiffer, Franz}}, issn = {{0956-7135}}, keywords = {{Dark-field imaging; Foreign object detection; Novelty detection; Phase-contrast imaging; Texture analysis; X-ray radiography}}, language = {{eng}}, month = {{09}}, pages = {{39--47}}, publisher = {{Elsevier}}, series = {{Food Control}}, title = {{Novelty detection of foreign objects in food using multi-modal X-ray imaging}}, url = {{http://dx.doi.org/10.1016/j.foodcont.2016.02.023}}, doi = {{10.1016/j.foodcont.2016.02.023}}, volume = {{67}}, year = {{2016}}, }