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DC Field | Value | Language |
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dc.contributor.author | Tzortzis, Ioannis N. | - |
dc.contributor.author | Rallis, Ioannis | - |
dc.contributor.author | Makantasis, Konstantinos | - |
dc.contributor.author | Doulamis, Anastasios | - |
dc.contributor.author | Doulamis, Nikolaos | - |
dc.contributor.author | Voulodimos, Athanasios | - |
dc.date.accessioned | 2024-08-20T09:31:45Z | - |
dc.date.available | 2024-08-20T09:31:45Z | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | Tzortzis, I. N., Rallis, I., Makantasis, K., Doulamis, A., Doulamis, N., & Voulodimos, A. (2022, October). Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery. IEEE International Conference on Image Processing ICIP2022, Bordeaux. 3136-3140. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/125533 | - |
dc.description.abstract | In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-R tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against overfitting. Experimental results on real-world data from UNESCO protected areas indicate the superiority of the proposed scheme compared to conventional deep learning models. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Hyperspectral imaging -- Classification | en_GB |
dc.subject | Tensor products | en_GB |
dc.subject | Cultural property -- Data processing -- Case studies | en_GB |
dc.subject | Imaging systems -- Remote sensing | en_GB |
dc.title | Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | IEEE International Conference on Image Processing ICIP 2022 | en_GB |
dc.bibliographicCitation.conferenceplace | Bordeaux, France, 16-19/10/2022. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/ICIP46576.2022.9897527 | - |
Appears in Collections: | Scholarly Works - FacICTAI |
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Automatic inspection of cultural monuments using deep and tensor based learning on hyperspectral imagery 2022.pdf Restricted Access | 4.31 MB | Adobe PDF | View/Open Request a copy |
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