Mohammadreza Amirian
Mohammadreza Amirian
ZHAW
School of Engineering
Forschungsschwerpunkt Information Engineering
Persönliches Profil
Tätigkeit an der ZHAW als
Wissenschaftlicher Assistent
Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse
Maschinelles Lernen, Mustererkennung, Neuronale Netze, Affective Computing, Gesichtsausdruck, Computer Vision, Digitale Signalverarbeitung, Bio-Physiologische Signalverarbeitung, Schmerzschätzung, Sonar, Bildverarbeitung, Komprimierte Erkennung, Medizinische Bildgebung, Multi-modale DatenFusion, Recurrent Networks
Aktuelle Forschungsinteressen: Deep Learning, Bildverarbeitung
Aus- und Fortbildung
Master, Kommunikationstechnik, 2017, Universität Ulm
Projekte
- Ada – Advanced Algorithms for an Artificial Data Analyst / Teammitglied / 01.10.2017
- QualitAI - Quality control of industrial products via deep learning on images / Teammitglied / 01.08.2017
- Libra: A One-Tool Solution for MLD4 Compliance / Teammitglied / 05.09.2016
- Complexity 4.0 / Teammitglied / 15.06.2016
Publikationen und Konferenzbeiträge
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Stadelmann, Thilo; Amirian, Mohammadreza; Arabaci, Ismail; Arnold, Marek; Duivesteijn, Gilbert François; Elezi, Ismail; Geiger, Melanie; Lörwald, Stefan; Meier, Benjamin Bruno; Rombach, Katharina; Tuggener, Lukas,
2018.
Deep learning in the wild [Paper].
In:
Proceedings of the 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR).
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, September 19-21, 2018.
IAPR.
Verfügbar unter: https://doi.org/10.21256/zhaw-3872
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Meier, Benjamin Bruno; Elezi, Ismail; Amirian, Mohammadreza; Dürr, Oliver; Stadelmann, Thilo,
2018.
Learning neural models for end-to-end clustering [Paper].
In:
Proceedings of the 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR).
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, September 2018.
IAPR.
Verfügbar unter: https://doi.org/10.21256/zhaw-3850
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Amirian, Mohammadreza; Schwenker, Friedhelm; Stadelmann, Thilo,
2018.
Trace and detect adversarial attacks on CNNs using feature response maps [Paper].
In:
Proceedings of the 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR).
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, September 19-21, 2018.
IAPR.
Verfügbar unter: https://doi.org/10.21256/zhaw-3863
Publikationen vor Tätigkeit an der ZHAW
Amirian, Mohammadreza; Kächele, Markus; Palm, Günther; Schwenker, Friedhelm. Support Vector Regression of Sparse Dictionary-based Features for View-Independent Action Unit Intensity Estimation. Facial Expression Recognition Challenge (FERA 2017).
Amirian, Mohammadreza; Kächele, Markus; Thiam, Patrik; Kessler, Viktor; Schwenker, Friedhelm. Continuous Multimodal Human Affect Estimation Using Echo State Networks. Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge (ACM 2016).
Amirian, Mohammadreza; Kächele, Markus; Schwenker, Friedhelm. Using Radial Basis Function Neural Networks for Continuous and Discrete Pain Estimation from Bio-physiological Signals. IAPR Workshop on Artificial Neural Networks in Pattern Recognition (2016).
Kächele, Markus; Thiam, Patrik; Amirian, Mohammadreza, Schwenker, Friedhelm; Palm, Günther. Methods for Person-Centered Continuous Pain Intensity Assessment from Bio-Physiological Channels. IEEE Journal of Selected Topics in Signal Processing (2016).
Kächele, Markus; Mohammadreza, Amirian; Thiam, Patrick; Werner, Philipp; Walter, Steffen; Palm, Günther; Schwenker, Friedhelm. "Adaptive Confidence Learning for the Personalization of Pain Intensity Estimation Systems." Evolving Systems.
Kazemi, Kamran; Amirian, Mohammadreza; Dehghani, Mohammad Javad. The S-transform Using a New Window to Improve Frequency and Time Resolutions. Signal, Image and Video Processing (2014).