dr. N. Strisciuglio (Nicola)

Universitair docent


Engineering & Materials Science
Acoustic Waves
Blood Vessels
Computer Vision
Convolutional Neural Networks


Riego Del Castillo, V., Sánchez-González, L., Campazas-Vega, A. , & Strisciuglio, N. (2022). Vision-Based Module for Herding with a Sheepdog Robot. Sensors (Basel, Switzerland), 22(14), [5321]. https://doi.org/10.3390/s22145321
Pandey, V. , Brune, C. , & Strisciuglio, N. (2022). Self-supervised Learning Through Colorization for Microscopy Images. In S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, & F. Tombari (Eds.), Image Analysis and Processing – ICIAP 2022: 21st International Conference, Lecce, Italy, May 23-27, 2022. Proceedings, Part II (pp. 621-632). (Lecture Notes in Computer Science; Vol. 13232). Springer. https://doi.org/10.1007/978-3-031-06430-2_52
Greco, A. , Strisciuglio, N., Vento, M. , & Vigilante, V. (2022). Benchmarking deep networks for facial emotion recognition in the wild. Multimedia tools and applications. https://doi.org/10.1007/s11042-022-12790-7
Brandt, R. , Strisciuglio, N., & Petkov, N. (2021). MTStereo 2.0: Accurate Stereo Depth Estimation via Max-Tree Matching. In N. Tsapatsoulis, A. Panayides, T. Theocharides, A. Lanitis, A. Lanitis, C. Pattichis, C. Pattichis, & M. Vento (Eds.), Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings (pp. 110-119). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13052 LNCS). Springer. https://doi.org/10.1007/978-3-030-89128-2_11
Strisciuglio, N., & Petkov, N. (2021). Brain-Inspired Algorithms for Processing of Visual Data. In K. Amunts, L. Grandinetti, T. Lippert, & N. Petkov (Eds.), Brain-Inspired Computing - 4th International Workshop, BrainComp 2019, Revised Selected Papers (pp. 105-115). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12339 LNCS). Springer. https://doi.org/10.1007/978-3-030-82427-3_8
Riego, V., Sánchez-González, L., Fernández-Robles, L., Gutiérrez-Fernández, A. , & Strisciuglio, N. (2021). Burr detection and classification using RUSTICO and image processing. Journal of computational science, 56, [101485]. https://doi.org/10.1016/j.jocs.2021.101485
Mehra, A. , Spreeuwers, L. , & Strisciuglio, N. (2021). Deepfake detection using capsule networks and long short-term memory networks. In G. M. Farinella, P. Radeva, J. Braz, & K. Bouatouch (Eds.), Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP (pp. 407-414). SCITEPRESS. https://doi.org/10.5220/0010289004070414
Melotti, D., Heimbach, K., Rodríguez-Sánchez, A. , Strisciuglio, N., & Azzopardi, G. (2020). A robust contour detection operator with combined push-pull inhibition and surround suppression. Information sciences, 524, 229-240. https://doi.org/10.1016/j.ins.2020.03.026
Brandt, R. , Strisciuglio, N., Petkov, N., & Wilkinson, M. H. F. (2020). Efficient binocular stereo correspondence matching with 1-D Max-Trees. Pattern recognition letters, 135, 402-408. https://doi.org/10.1016/j.patrec.2020.02.019
Strisciuglio, N., Lopez-Antequera, M., & Petkov, N. (2020). Enhanced Robustness of Convolutional Networks with a Push-Pull Inhibition Layer. Neural Computing and Applications, 32(24), 17957-17971. https://doi.org/10.1007/s00521-020-04751-8
Ramachandran, S. , Strisciuglio, N., Vinekar, A., John, R., & Azzopardi, G. (2020). U-COSFIRE filters for vessel tortuosity quantification with application to automated diagnosis of retinopathy of prematurity. Neural Computing and Applications, 32(16), 12453-12468. https://doi.org/10.1007/s00521-019-04697-6
Leyva-Vallina, M. , Strisciuglio, N., Lopez Antequera, M., Tylecek, R., Blaich, M., & Petkov, N. (2019). TB-places: A data set for visual place recognition in garden environments. IEEE Access, 7, 52277-52287. [8698240]. https://doi.org/10.1109/ACCESS.2019.2910150

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Vakken Collegejaar  2022/2023

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.

Vakken Collegejaar  2021/2022



Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (gebouwnr. 11), kamer 4122
Hallenweg 19
7522NH  Enschede

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Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling  4122
Postbus 217
7500 AE Enschede

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