<div class="csl-bib-body">
<div class="csl-entry">Jonak, M., Dorazil, J., Kolařík, M., Jezek, S., Burget, R., & Kotrlý, M. (2023). Forensic Comparison of Soil Particles Using Gaussian Mixture Models and Likelihood Ratio Test. In <i>ICUMT 2023 : the 15th International Congress on Ultra Modern Telecommunications and Control Systems</i> (pp. 188–192). https://doi.org/10.1109/ICUMT61075.2023.10333101</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/199605
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dc.description.abstract
Forensic analysis of soil traces can be highly valuable in criminal investigation as it can provide evidence which links a person or a contaminated object with one specific location. Given two soil samples, the task of a forensic expert is typically to decide whether they both originate from the same location or not. To confidently answer this question it is necessary to perform a complex analysis focused on examination of the sample's organic, anthropogenic, and naturally occurring components. In this paper, we focus on one element of the analysis which studies small mineral particles within the sample. In particular, we propose a novel method for automatic comparison of soil particles, using scanning electron microscope images acquired in the secondary electron (SE) or backscattered electron mode. The method involves segmentation of particles, identification of their contours, and extraction of local descriptors from the images, which are then used to train a sample specific and non-specific Gaussian mixture model (GMM). Finally, a likelihood ratio, based on the GMMs, is calculated to assess the odds that two samples originate from the same location. The proposed method, utilizing Root SIFT descriptors extracted from the SE images along the particle contours, achieved an equal error rate of 13.1 % and an area under the curve of 95.2 %, surpassing our baseline method derived from particle size analysis.
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dc.language.iso
en
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dc.subject
deep learning
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dc.subject
Gaussian mixture model
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dc.subject
scanning electron microscopy
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dc.subject
segmentation
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dc.subject
soil analysis
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dc.title
Forensic Comparison of Soil Particles Using Gaussian Mixture Models and Likelihood Ratio Test
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
Brno University of Technology, Czechia
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dc.contributor.affiliation
Brno University of Technology, Czechia
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dc.contributor.affiliation
Brno University of Technology, Czechia
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dc.contributor.affiliation
Brno University of Technology, Czechia
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dc.contributor.affiliation
Policie České republiky - Kriminalistický ústav, Czechia
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dc.relation.isbn
979-8-3503-9328-6
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dc.description.startpage
188
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dc.description.endpage
192
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
ICUMT 2023 : the 15th International Congress on Ultra Modern Telecommunications and Control Systems
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tuw.researchTopic.id
X1
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tuw.researchTopic.name
Beyond TUW-research foci
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E389-03 - Forschungsbereich Signal Processing
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tuw.publisher.doi
10.1109/ICUMT61075.2023.10333101
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dc.description.numberOfPages
5
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tuw.author.orcid
0000-0001-6715-5865
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tuw.author.orcid
0000-0001-6158-6162
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tuw.author.orcid
0000-0002-9726-4103
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tuw.author.orcid
0000-0003-1849-5390
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tuw.author.orcid
0000-0003-1086-7196
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tuw.event.name
2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops