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Institut for Elektro- og Computerteknologi

Publications

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Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2017). Laplacian One Class Extreme Learning Machine for Human Action Recognition. I 2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016 Artikel 7813387 IEEE. https://doi.org/10.1109/MMSP.2016.7813387

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2016). One Class Classification applied in Facial Image Analysis. I 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (s. 1644-1648). Artikel 7532637 IEEE. https://doi.org/10.1109/ICIP.2016.7532637

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2016). Kernel Subclass Support Vector Description for Face and Human Action Recognition. I 2016 1st International Workshop on Sensing, Processing and Learning for Intelligent Machines, SPLINE 2016 - Proceedings Artikel 7528409 IEEE. https://doi.org/10.1109/SPLIM.2016.7528409

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2015). Large-scale classification by an Approximate Least Squares One-Class Support Vector Machine ensemble. I Trustcom/BigDataSE/ISPA, 2015 IEEE (s. 6-10). IEEE. https://doi.org/10.1109/Trustcom.2015.555

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2015). Exploiting subclass information in one-class support vector machine for video summarization. I Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (s. 2259-2263). IEEE. https://doi.org/10.1109/ICASSP.2015.7178373

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2014). Video summarization based on subclass Support Vector Data Description. I Computational Intelligence for Engineering Solutions (CIES), 2014 IEEE Symposium on IEEE. https://doi.org/10.1109/CIES.2014.7011849

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2014). Supervised video summarization by content-based video segment selection. I European Conference on Visual Media Production (CVMP) University of Bristol. https://core.ac.uk/download/pdf/73983041.pdf

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., 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. (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. Artikel 9366908. https://doi.org/10.1109/JSTARS.2021.3062936

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2021). IrradianceNet: Spatiotemporal Deep Learning Model for Satellite-Derived Solar Irradiance Nowcasting. Solar Energy, 228, 659-669. https://doi.org/10.1016/j.solener.2021.09.073

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2022). Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data. Scientific Reports, 12(1), Artikel 8395. https://doi.org/10.1038/s41598-022-12167-8

Nousi, P., Tsantekidis, A., Passalis, N., Ntakaris, A., Kanniainen, J., Tefas, A., Gabbouj, M. & Iosifidis, A. (2019). Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data. IEEE Access, 7, 64722 - 64736 . Artikel 8713851. https://doi.org/10.1109/ACCESS.2019.2916793

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

Ntakaris, A., Magris, M., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2018). Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods. Journal of Forecasting, 37(8), 852-866. https://doi.org/10.1002/for.2543

Ntakaris, A., Mirone, G., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Feature Engineering for Mid-Price Prediction with Deep Learning. IEEE Access, 7, 82390 - 82412. Artikel 8743410. https://doi.org/10.1109/ACCESS.2019.2924353

Ntakaris, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Mid-price prediction based on machine learning methods with technical and quantitative indicators. PLOS ONE, 15(6 June), Artikel e0234107. https://doi.org/10.1371/journal.pone.0234107

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

Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2022). Variational Neural Networks implementation in Pytorch and JAX[Formula presented]. Software Impacts, 14, Artikel 100431. https://doi.org/10.1016/j.simpa.2022.100431

Oleksiienko, I. & Iosifidis, A. (2022). 3D object detection and tracking. I A. Iosifidis & A. Tefa (red.), Deep Learning for Robot Perception and Cognition (s. 313-340). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00018-X

Oleksiienko, I., Nousi, P., Passalis, N., Tefas, A. & Iosifidis, A. (2023). Variational Voxel Pseudo Image Tracking. I 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (s. 323-328). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371810

Oleksiienko, I. & Iosifidis, A. (2023). Layer Ensembles. I D. Comminiello & M. Scarpiniti (red.), 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) IEEE. https://doi.org/10.1109/MLSP55844.2023.10286005

Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2023). Variational Neural Networks. Procedia Computer Science, 222, 104-113. https://doi.org/10.1016/j.procs.2023.08.148

Oleksiienko, I., Nousi, P., Passalis, N., Tefas, A. & Iosifidis, A. (2024). Vpit: real-time embedded single object 3D tracking using voxel pseudo images. Neural Computing and Applications, 36(32), 20341-20354. https://doi.org/10.1007/s00521-024-10259-2

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. 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

Passalis, N., Tsantekidis, A., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2017). Time-series Classification using Neural Bag-of-Features. Proceedings of the European Signal Processing Conference, 2017, 311-315. https://doi.org/10.23919/EUSIPCO.2017.8081217

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). Artikel 8682297 IEEE. https://doi.org/10.1109/ICASSP.2019.8682297

Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2020). Variance-preserving Deep Metric Learning for Content-based Image Retrieval. Pattern Recognition Letters, 131, 8-14. https://doi.org/10.1016/j.patrec.2019.11.041

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Temporal Bag-of-Features Learning for Predicting Mid Price Movements using High Frequency Limit Order Book Data. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(6), 774-785. https://doi.org/10.1109/TETCI.2018.2872598

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 2019 - Proceedings (s. 854-858). Artikel 8802945 IEEE. https://doi.org/10.1109/ICIP.2019.8802945

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). Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data. Pattern Recognition Letters, 136, 183-189. https://doi.org/10.1016/j.patrec.2020.06.006

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). Artikel 9054321 IEEE. https://doi.org/10.1109/ICASSP40776.2020.9054321

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

Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2021). Hypersphere-Based Weight Imprinting for Few-Shot Learning on Embedded Devices. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 925-930. https://doi.org/10.1109/TNNLS.2020.2979745

Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2021). Robust hypersphere-based weight imprinting for few-shot learning. I 2020 28th European Signal Processing Conference (EUSIPCO (s. 1392-1396). Artikel 9287340 IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287340

Passalis, N., Kanniainen, J., Gabbouj, M., Iosifidis, A. & Tefas, A. (2021). Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization. Journal of Signal Processing Systems, 93(10), 1235-1251. https://doi.org/10.1007/s11265-020-01624-0

Patrona, F., Iosifidis, A., Tefas, A., Nikolaidis, N. & Pitas, I. (2016). Visual Voice Activity Detection in the Wild. IEEE Transactions on Multimedia, 18(6), 967-977. Artikel 7420734. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7420734

Patrona, F., Iosifidis, A., Tefas, A., Nikolaidis, N. & Pitas, I. (2015). Visual Voice Activity Detection based on Spatiotemporal Information and Bag of Words. I Image Processing (ICIP), 2015 IEEE International Conference on (s. 2334-2338). IEEE. https://doi.org/10.1109/ICIP.2015.7351219

Pauwels, R. & Iosifidis, A. (2024). Deep Learning in Image Processing: Part 1—Types of Neural Networks, Image Segmentation. I Artificial Intelligence in Dentistry (s. 283-316). Springer. https://doi.org/10.1007/978-3-031-43827-1_17

Pauwels, R. & Iosifidis, A. (2024). Deep Learning in Image Processing: Part 2—Image Enhancement, Reconstruction and Registration. I K. Orhan & R. Jagtap (red.), Artificial Intelligence in Dentistry (s. 317-351). Springer. https://doi.org/10.1007/978-3-031-43827-1_18

Rafiei Foroushani, M., Raitoharju, J. & Iosifidis, A. (2023). Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey. IEEE Access, 11, 2445 - 2477. https://doi.org/10.1109/ACCESS.2023.3234187

Rafiei Foroushani, M. & Iosifidis, A. (2023). Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval. I 2023 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN54540.2023.10191068

Rafiei Foroushani, M., Tran, D. T. & Iosifidis, A. (2023). Recognition of Defective Mineral Wool Using Pruned ResNet Models. I H. Dorksen, S. Scanzio, J. Jasperneite, L. Wisniewski, K. F. Man, T. Sauter, L. Seno, H. Trsek & V. Vyatkin (red.), 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) IEEE. https://doi.org/10.1109/INDIN51400.2023.10217993

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 Proceedings - 2nd Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2016 - In Conjunction with International Conference on Pattern Recognition, ICPR 2016 (s. 43-48). Artikel 7813092 IEEE. https://doi.org/10.1109/CVAUI.2016.20

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

Riabchenko, E., Meissner, K., Ahmad, I., Iosifidis, A., Tirronen, V., Gabbouj, M. & Kiranyaz, S. (2016). Learned vs. Engineered Features for Fine-Grained Classification of Aquatic Macroinvertebrates. I 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (s. 2276-2281). Artikel 7899975 IEEE. https://doi.org/10.1109/ICPR.2016.7899975

Samiee, K., Iosifidis, A. & Gabbouj, M. (2017). On the comparison of random and Hebbian weights for the training of single-hidden layer feedforward neural networks. Expert Systems with Applications, 83, 177-186. https://doi.org/10.1016/j.eswa.2017.04.025

Schiopu, I., Gabbouj, M., Iosifidis, A., Zeng, B. & Liu, S. (2017). Subaperture Image Segmentation for Lossless Compression. I The seventh International Conference on Image Processing Theory, Tools and Applications http://www.cs.tut.fi/~iosifidi/files/conference/2017_IPTA_SAIC.pdf?dl=0

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Alexandros Iosifidis