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DC Field | Value | Language |
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dc.contributor.author | Makantasis, Konstantinos | - |
dc.contributor.author | Doulamis, Anastasios | - |
dc.contributor.author | Loupos, Konstantinos | - |
dc.date.accessioned | 2024-08-12T10:40:50Z | - |
dc.date.available | 2024-08-12T10:40:50Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Makantasis, K., Doulamis, A., & Loupos, K. (2015). Deep learning-based man-made object detection from hyperspectral data. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, I. Pavlidis, R. Feris.,…G. Weber (Eds.), Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science, vol. 9474 (pp. 693-705). Springer International Publishing. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/125371 | - |
dc.description.abstract | We propose a Gaussian mixture model with fixed but unknown number of components for background subtraction in infrared imagery. Following a Bayesian approach, our method automatically estimates the number of components as well as their parameters, while simultaneously it avoids over/under fitting. The equations for estimating model parameters are analytically derived and thus our method does not require any sampling algorithm that is computationally and memory inefficient. The pixel density estimate is followed by an efficient and highly accurate updating mechanism, which permits our system to be automatically adapted to dynamically changing visual conditions. Experimental results and comparisons with other methods indicate the high potential of the proposed method while keeping computational cost suitable for real-time applications. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Infrared imaging | en_GB |
dc.subject | Gaussian distribution -- Data processing | en_GB |
dc.subject | Bayesian statistical decision theory | en_GB |
dc.subject | eVACUATE (Project) | en_GB |
dc.subject | Image analysis -- Mathematical models | en_GB |
dc.title | Variational inference for background subtraction in infrared imagery | en_GB |
dc.title.alternative | Advances in visual computing. ISVC 2015. Lecture notes in computer science vol. 9474 | 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 | Advances in Visual Computing: 11th International Symposium, ISVC 2015 | en_GB |
dc.bibliographicCitation.conferenceplace | Las Vegas, NV, USA, 14-16/Dec/2015. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1007/978-3-319-27857-5_62 | - |
Appears in Collections: | Scholarly Works - FacICTAI |
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Variational inference for background subtraction in infrared imagery 2015.pdf Restricted Access | 796.6 kB | Adobe PDF | View/Open Request a copy |
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