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  • 1
    Online Resource
    Online Resource
    IOP Publishing ; 2017
    In:  Physics in Medicine & Biology Vol. 62, No. 19 ( 2017-09-21), p. 7784-7797
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 62, No. 19 ( 2017-09-21), p. 7784-7797
    Type of Medium: Online Resource
    ISSN: 1361-6560
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2017
    detail.hit.zdb_id: 1473501-5
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2021
    In:  Journal of Imaging Vol. 7, No. 7 ( 2021-06-24), p. 104-
    In: Journal of Imaging, MDPI AG, Vol. 7, No. 7 ( 2021-06-24), p. 104-
    Abstract: X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.
    Type of Medium: Online Resource
    ISSN: 2313-433X
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2824270-1
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  • 3
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2023
    In:  Geophysical Journal International
    In: Geophysical Journal International, Oxford University Press (OUP)
    Abstract: Absolute paleointensities are notoriously hard to obtain, because conventional thermal Thellier paleointensity experiments often have low success rates for volcanic samples. The thermal treatments necessary for these experiments potentially induce (magnetic) alteration in the samples, preventing a reliable paleointensity estimate. These heating steps can be avoided by pseudo-Thellier measurements, where samples are demagnetized and remagnetized with alternating-fields. However, pseudo-Thellier experiments intrinsically produce relative paleointensities. Over the past years attempts were made to calibrate pseudo-Thellier results into absolute paleointensities for lavas by mapping laboratory induced Anhysteretic Remanent Magnetizations (ARMs) to the thermally acquired Natural Remanent Magnetizations (NRMs). Naturally occurring volcanic rocks, however, are assemblages of minerals differing in grain size, shape, and chemistry. These different minerals all have their own characteristic mapping between ARMs and thermal NRMs Here we show that it is possible to find these characteristic mappings by unmixing the NRM demagnetization and the ARM acquisition curves into end-members, with an iterative method of non-negative matrix factorization. In turn, this end-member modeling approach (EMMA) allows for the calculation of absolute paleointensities from pseudo-Thellier measurements. We tested our end-member modeling approach using a noise-free numerical data set, yielding a perfect reconstruction of the paleointensities. When adding noise up to levels beyond what is expected in natural samples, the end-member model still produces the known paleointensities well. In addition, we made a synthetic dataset with natural volcanic samples from different volcanic edifices that were given a magnetization by heating and cooling them in a controlled magnetic field in the lab. The applied fields ranged between 10 and 70 $\mu T$. The average absolute difference between the calculated paleointensity and the known lab-field is around $10\ \mu T$ for the models with 2 to 4 end-members, while the paleointensity of almost all flows can be retrieved within a deviation of ± $20\ \mu T$. The average difference between calculated paleointensities for the 3 end-member model is -1.7 $\mu T$. The deviations between the paleointensities and the known lab-fields are almost Gaussian distributed around the expected values. To assess whether the end-members produced by our analysis have a physical meaning, we measured the Curie temperatures of our samples. These Curie measurements show that there is a relationship between the abundances of the end members of the 3 end-member model in the samples and their dominant Curie temperatures. This indicates that even whilst the spectrum of Curie temperatures and hence composition of iron-oxides in the sample set is continuous, and the magnetization is also related to mineral size and shape, the calculated end-members of the 3 end-member model are somewhat related to magnetic mineral composition present in the samples. Although the two datasets in our study show that there is potential for using this end-member modeling technique for finding absolute paleointensities from pseudo-Thellier data, these synthetic datasets cannot be directly related to natural samples. Therefore, it is necessary to compile a dataset of known paleointensities from different volcanic sites that recently cooled in a known magnetic field to find the universal end-members in future studies.
    Type of Medium: Online Resource
    ISSN: 0956-540X , 1365-246X
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 3042-9
    detail.hit.zdb_id: 2006420-2
    detail.hit.zdb_id: 1002799-3
    SSG: 16,13
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  • 4
    In: Journal of Imaging, MDPI AG, Vol. 6, No. 12 ( 2020-12-02), p. 132-
    Abstract: An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that may be essential for the imaging task may not be preserved in the data reduction step. Convolutional neural network (CNN) approaches are capable of learning the specific features relevant to the particular imaging task, but applying them directly to the spectral input data is constrained by the computational efficiency. We propose a novel supervised deep learning approach for combining data reduction and image analysis in an end-to-end architecture. In our approach, the neural network component that performs the reduction is trained such that image features most relevant for the task are preserved in the reduction step. Results for two convolutional neural network architectures and two types of generated datasets show that the proposed Data Reduction CNN (DRCNN) approach can produce more accurate results than existing popular data reduction methods, and can be used in a wide range of problem settings. The integration of knowledge about the task allows for more image compression and higher accuracies compared to standard data reduction methods.
    Type of Medium: Online Resource
    ISSN: 2313-433X
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2824270-1
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  • 5
    In: Heritage Science, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2023-06-19)
    Abstract: Computed tomography (CT) is a powerful non-invasive tool to analyze cultural heritage objects by allowing museum professionals to obtain 3D information about the objects’ interior. These insights can help with the conservation or restoration of the objects, as well as provide contextual information on the objects’ history or making process. Cultural heritage objects exist in a wide variety and have characteristics which present challenges for CT scanning: multi-scale internal features, a diversity of sizes and shapes, and multi-material objects. Because X-ray absorption is related to the density, thickness of the material, and atomic composition, the challenges are greater when the object consists of multiple different materials with varying densities. This is especially true for cases with extreme density contrasts such as that between metals and textiles. An untailored acquisition of CT scans of multi-material objects can lead to reduced image quality and heavy visual errors called image artifacts, which can influence the perception or representation of information. A tailored acquisition can reduce these artifacts and lead to a higher information gain. In this work, we firstly discuss how the X-ray beam properties and the beam-object interaction influence CT image formation and how to use filters to manipulate the emitted X-ray beam to improve image quality for multi-material objects. We showcase that this can be achieved with limited resources in a low-cost DIY fashion with thin sheets of metal as filters, 3D-printed filter frames and a filter holder. Secondly, we give a qualitative analysis of the influence of the CT acquisition parameters illustrated with two case study objects from the textile collection of the Rijksmuseum, Amsterdam, The Netherlands. With this we provide insights and intuitions on tailoring the CT scan to the cultural heritage objects. Thirdly, we extract a general concept of steps for museum professionals to design an object-tailored CT scan for individual cases.
    Type of Medium: Online Resource
    ISSN: 2050-7445
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2710672-X
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  • 6
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Scientific Reports Vol. 13, No. 1 ( 2023-02-02)
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2023-02-02)
    Abstract: Although X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time. However, this approach depends heavily on the availability of a large amount of data, strongly hampering the viability of industrial use with high variability between batches of products. We present a computationally efficient, CT-based approach for creating artificial single-view X-ray data based on just a few physically CT-scanned objects. By algorithmically modifying the CT-volume, a large variety of training examples is obtained. Our results show that applying the generative model to a single CT-scanned object results in image analysis accuracy that would otherwise be achieved with scans of tens of real-world samples. Our methodology leads to a strong reduction in training data needed, improved coverage of the combinations of base and foreign objects, and extensive generalizability to additional features. Once trained on just a single CT-scanned object, the resulting deep neural network can detect foreign objects in real-time with high accuracy.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2615211-3
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  • 7
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2022
    In:  IEEE Transactions on Computational Imaging Vol. 8 ( 2022), p. 598-608
    In: IEEE Transactions on Computational Imaging, Institute of Electrical and Electronics Engineers (IEEE), Vol. 8 ( 2022), p. 598-608
    Type of Medium: Online Resource
    ISSN: 2333-9403 , 2334-0118 , 2573-0436
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2022
    detail.hit.zdb_id: 2806107-X
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  • 8
    In: Postharvest Biology and Technology, Elsevier BV, Vol. 211 ( 2024-05), p. 112814-
    Type of Medium: Online Resource
    ISSN: 0925-5214
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 1498582-2
    SSG: 12
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  • 9
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2016
    In:  Numerical Algorithms Vol. 71, No. 3 ( 2016-3), p. 673-697
    In: Numerical Algorithms, Springer Science and Business Media LLC, Vol. 71, No. 3 ( 2016-3), p. 673-697
    Type of Medium: Online Resource
    ISSN: 1017-1398 , 1572-9265
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
    detail.hit.zdb_id: 2002650-X
    SSG: 17,1
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  • 10
    In: Powder Technology, Elsevier BV, Vol. 434 ( 2024-02), p. 119269-
    Type of Medium: Online Resource
    ISSN: 0032-5910
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2019938-7
    detail.hit.zdb_id: 208997-X
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