Publications

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Malik, J., Kiranyaz, S., Al-Raoush, R. I., Monga, O., Garnier, P., Foufou, S., Bouras, A., Iosifidis, A., Gabbouj, M. & Baveye, P. C. (2022). 3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images. Computers & Geosciences, 159, [105017]. https://doi.org/10.1016/j.cageo.2021.105017

Iosifidis, A. & Gabbouj, M. (2015). A Bayesian Approach for Extreme Learning Machine-based Subspace Learning. I Signal Processing Conference (EUSIPCO), 2015 23rd European (s. 2356-2360). IEEE. https://doi.org/10.1109/EUSIPCO.2015.7362806

Iosifidis, A., Mouroutsos, S. G. & Gasteratos, A. (2011). A hybrid static/active video surveillance system. International Journal of Optomechatronics, 5(1), 80-95. https://doi.org/10.1080/15599612.2011.553252

Iosifidis, A., Tefas, A., Pitas, I. & Gabbouj, M. (2016). A review of approximate methods for kernel-based Big Media Data Analysis. I Signal Processing Conference (EUSIPCO), 2016 24th European (s. 1113-1117). IEEE. https://doi.org/10.1109/EUSIPCO.2016.7760421

Muravev, A., Thanh Tran, D., Iosifidis, A., Kiranyaz, S. & Gabbouj, M. (2018). Acceleration approaches for big data analysis. I 2018 25th IEEE International Conference on Image Processing (ICIP): Proceedings (s. 311-315). IEEE. https://doi.org/10.1109/ICIP.2018.8451082

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Active Classification for Human Action Recognition. I Image Processing (ICIP), 2013 20th IEEE International Conference on (s. 3249-3253). IEEE. https://doi.org/10.1109/ICIP.2013.6738669

Iosifidis, A., Tefas, A. & Pitas, I. (2011). Activity based Person Identification using Fuzzy Representation and Discriminant Learning. IEEE Transactions on Information Forensics and Security, 7(2), 530-542. https://doi.org/10.1109/TIFS.2011.2175921

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Adaptive Normalization for Forecasting Limit Order Book Data Using Convolutional Neural Networks. I 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (s. 1713-1717). [9054321] IEEE. https://doi.org/10.1109/ICASSP40776.2020.9054321

Magris, M., Ntakaris, A., Kanniainen, J., Iosifidis, A. & Gabbouj, M. (2016). Analysis of high-frequency order flow dynamics with machine learning techniques. I 10th International Conference on Computational and Financial Econometrics

Oleksiienko, I. & Iosifidis, A. (2021). Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems. I N. Mallenahalli, A. Bhattacharya, S. Senatore, A. Negi & A. Hirose (red.), 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) (s. 59-64). IEEE. https://doi.org/10.1109/ICETCI51973.2021.9574075

Magris, M. & Iosifidis, A. (2021). Approximate Bayes factors for unit root testing. Paper præsenteret ved International Association for Applied Econometrics Annual Conference 2021, Rotterdam, Holland. https://www.researchgate.net/publication/349492007_Approximate_Bayes_factors_for_unit_root_testing

Iosifidis, A., Tefas, A. & Pitas, I. (2016). Approximate Kernel Extreme Learning Machine for Large-Scale Data Classification. Neurocomputing, 219, 210-220. https://doi.org/10.1016/j.neucom.2016.09.023

Tran, D. T., Passalis, N., Tefas, A., Gabbouj, M. & Iosifidis, A. (2022). Attention-Based Neural Bag-of-Features Learning for Sequence Data. IEEE Access, 10, 45542-45552. https://doi.org/10.1109/ACCESS.2022.3169776

Ärje, J., Milioris, D., Tran, D. T., Jepsen, J. U., Raitoharju, J., Gabbouj, M., Iosifidis, A. & Høye, T. T. (2019). Automatic Flower Detection and Classification System Uing a Light-Weight Convolutional Neural Network. I 27th European Signal Processing Conference EUSIPCO 2019 IEEE. Proceedings of the European Signal Processing Conference http://eusipco2019.org/Proceedings/papertitles.html

Thanh Tran, D., Høye, T. T., Gabbouj, M. & Iosifidis, A. (2018). Automatic Flower and Visitor Detection System. Proceedings of the European Signal Processing Conference, 2018, 405-409. https://doi.org/10.23919/EUSIPCO.2018.8553494

Ärje, J., Melvad, C., Jeppesen, M. R., Madsen, S. A., Raitoharju, J., Rasmussen, M. S., Iosifidis, A., Tirronen, V., Gabbouj, M., Meissner, K. & Høye, T. T. (2020). Automatic image-based identification and biomass estimation of invertebrates. Methods in Ecology and Evolution, 11(8), 922-931. https://doi.org/10.1111/2041-210X.13428

Laakom, F., Passalis, N., Raitoharju, J., Nikkanen, J., Tefas, A., Iosifidis, A. & Gabbouj, M. (2020). Bag of Color Features for Color Constancy. IEEE Transactions on Image Processing, 29, 7722-7734. [9130881]. https://doi.org/10.1109/TIP.2020.3004921

Raitoharju, J., Riabchenko, E., Ahmad, I., Iosifidis, A., Gabbouj, M., Kiranyaz, S., Tirronen, V., Ärje, J., Kärkkäinen, S. & Meissner, K. (2018). Benchmark Database for Fine-Grained Image Classification of Benthick Macroinvertebrates. Image and Vision Computing, 78, 73-83. https://doi.org/10.1016/j.imavis.2018.06.005

Ntakaris, A., Magris, M., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2017). Benchmark Dataset for Mid-Price Prediction of Limit Order Book data. arXiv. https://arxiv.org/pdf/1705.03233.pdf

Iosifidis, A., Tefas, A., Pitas, I. & Gabbouj, M. (2017). Big Media Data Analysis. Signal Processing: Image Communication, 59, 105-108. https://doi.org/10.1016/j.image.2017.10.004

Waris, M. A., Iosifidis, A. & Gabbouj, M. (2017). CNN-based Edge Filtering for Object Proposals. Neurocomputing, 266, 631-640. https://doi.org/10.1016/j.neucom.2017.05.071

Iosifidis, A. (2020). Class mean vector component and discriminant analysis. Pattern Recognition Letters, 140, 207-213. https://doi.org/10.1016/j.patrec.2020.10.014

Iosifidis, A. & Gabbouj, M. (2017). Class-Specific Kernel Discriminant Analysis based on Cholesky Decomposition. I Neural Networks (IJCNN), 2017 International Joint Conference on (s. 1141-1146). IEEE. https://doi.org/10.1109/IJCNN.2017.7965980

Iosifidis, A. & Gabbouj, M. (2017). Class-Specific Kernel Discriminant Analysis revisited: further analysis and extensions. IEEE Transactions on Cybernetics, 47(12), 4485-4496. https://doi.org/10.1109/TCYB.2016.2612479

Passalis, N., Tefas, A., Iosifidis, A. & Gabbouj, M. (2019). Class-based Variational Representation Learning for Robust Image Retrieval. I 2019 IEEE International Conference on Image Processing (ICIP) (s. 854-858). IEEE. https://doi.org/10.1109/ICIP.2019.8802945

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Class-specific Reference Discriminant Analysis with application in Human Behavior Analysis. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 45(3), 315-326. https://doi.org/10.1109/THMS.2014.2379274

Iosifidis, A., Gabbouj, M. & Pekki, P. (2015). Class-specific nonlinear projections using class-specific kernel spaces. I Trustcom/BigDataSE/ISPA, 2015 IEEE (s. 17-24). IEEE. https://doi.org/10.1109/Trustcom.2015.557

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Class-specific nonlinear subspace learning based on optimized class representation. I Signal Processing Conference (EUSIPCO), 2015 23rd European (s. 2491-2495). IEEE. https://doi.org/10.1109/EUSIPCO.2015.7362833

Nielsen, A. H., Wagner, A., Iosifidis, A. & Karstoft, H. (2020). Cloud Nowcasting on Satellite Images: A Novel Dataset and Experimental Comparisons. https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/365696

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2020). CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds. Abstract fra European Conference on Computer Vision Workshop . https://drive.google.com/file/d/1_TV20JCIyBaetN6Y1mt7K5FeMx431Rr3/view

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2021). CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3485-3494. https://doi.org/10.1109/JSTARS.2021.3062936

Li, N., Iosifidis, A. & Zhang, Q. (2022). Collaborative edge computing for distributed CNN inference acceleration using receptive field-based segmentation. Computer Networks, 214, [109150]. https://doi.org/10.1016/j.comnet.2022.109150

Laakom, F., Raitoharju, J., Iosifidis, A., Nikkanen, J. & Gabbouj, M. (2019). Color Constancy Convolutional Autoencoder. https://arxiv.org/abs/1906.01340

Iosifidis, A. & Gabbouj, M. (2016). Combining Multi-class Maximum Margin Classification with Linear Discriminant Analysis for Human Action Recognition. I Image Processing (ICIP), 2016 IEEE International Conference on (s. 4180-4184). IEEE. https://doi.org/10.1109/ICIP.2016.7533147

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Computational Intelligence Approaches for Digital Media Analysis and Description. I J-S. Pan, V. Snasel, E. S. Corchado, A. Abraham & S-L. Wang (red.), Intelligent Data analysis and its Applications, Volume II (Bind 2, s. 263-272). Springer. Advances in Intelligent Systems and Computing Nr. 298 https://doi.org/10.1007/978-3-319-07773-4_26

Iosifidis, A. (2013). Computer Vision and Pattern Recognition Techniques for Human-Computer Interaction.

Panagiotatos, G., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Curriculum-based Teacher Ensemble for Robust Neural Network Distilation. I 27th European Signal Processing Conference EUSIPCO 2019 IEEE. Proceedings of the European Signal Processing Conference http://eusipco2019.org/Proceedings/papertitles.html

Panagiotatos, G., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Curriculum-based teacher ensemble for robust neural network distillation. I 2019 27th European Signal Processing Conference (EUSIPCO) IEEE. https://doi.org/10.23919/EUSIPCO.2019.8903112

Iosifidis, A. (2020). Danish researchers develop artificial intelligence to expose stock-market fraud. I J. Bruun (red.), Profile 2020 (s. 94-95). Aarhus Universitet, Institut for Ingeniørvidenskab . https://www.e-pages.dk/aarhusuniversitet/2231/html5/

Raitoharju, J., Riabchenko, E., Meissner, K., Ahmad, I., Iosifidis, A., Gabbouj, M. & Kiranyaz, S. (2017). Data Enrichment in Fine-Grained Classification of Aquatic Macroinvertebrates. I Computer Vision for Analysis of Underwater Imagery (CVAUI), 2016 ICPR 2nd Workshop on (s. 43-48). IEEE. https://doi.org/10.1109/CVAUI.2016.20

Böttjer, T., Ørnskov Rønsch, G., Gonçalves Gomes, C. Â., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Ijenction Moulds. I A-L. Andersen, R. Andersen, D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconﬁgurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (s. 431-439). Springer. Lecture Notes in Mechanical Engineering https://www.springerprofessional.de/en/data-driven-identification-of-remaining-useful-life-for-plastic-/19816878

Böttjer, T., Ørnskov Rønsch, G., Gomes, C., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconﬁgurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (s. 431-439). Springer. Lecture Notes in Mechanical Engineering https://doi.org/10.1007/978-3-030-90700-6_49

Thanh Tran, D., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Data-driven Neural Architecture Learning for Financial Time-series Forecasting. I Digital Image and Signal Processing https://arxiv.org/ftp/arxiv/papers/1903/1903.06751.pdf

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Deep Adaptive Input Normalization for Time Series Forecasting. arXiv.org. https://arxiv.org/pdf/1902.07892.pdf

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Deep Adaptive Input Normalization for Time Series Forecasting. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3760-3765. https://doi.org/10.1109/TNNLS.2019.2944933

Iosifidis, A. & Tefas, A. (2020). Deep Learning for Visual Content Analysis. Signal Processing: Image Communication, 83(April), [115806]. https://doi.org/10.1016/j.image.2020.115806

Cao, G., Iosifidis, A., Gabbouj, M., Raghavan, V. & Gottumukkala, R. (2021). Deep Multi-view Learning to Rank. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1426-1438. [8845659]. https://doi.org/10.1109/TKDE.2019.2942590

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series. I 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (s. 7545-7549). [8682297] IEEE. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings Bind 2019 May https://doi.org/10.1109/ICASSP.2019.8682297

Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C. & Raitoharju, J. (2021). Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences of the United States of America, 118(2), [e2002545117]. https://doi.org/10.1073/pnas.2002545117

Leporowski, B. T., Tola, D., Hansen, C. & Iosifidis, A. (2022). Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. I A-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (s. 224-232). Springer. Lecture Notes in Mechanical Engineering https://doi.org/10.1007/978-3-030-90700-6_25

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Revideret 06.09.2022

Alexandros Iosifidis