Aix-Marseille Université
Polytech Marseille Sud
Laboratoire d’Informatique et Systèmes (LIS – UMR 7020)
Equipe G-Mod
Campus de Luminy – Case 925
13288 Marseille Cedex 09

Secrétariat : 04 91 82 85 10


Retrouver tous les cours sur ma plateforme numérique : https://sebastien-mavromatis.pedaweb.univ-amu.fr


Sébastien Mavromatis received the Ph.D. degree in Computer Science in 2001 and the “Accreditation to Supervise Research” in 2019 from Aix-Marseille University. He is currently an associate professor at Aix-Marseille University. He is a member of the Laboratory of Computer Science and Systems (LIS – CNRS UMR 7020). He is regularly involved in many international cooperation projects in China, Peru and Algeria. He also participates to several national research and development projects with French and international industries. He is involved in journal editorial boards, conference organization, and scientific associations. His current research interests include Remote Sensing, Image Analysis, Virtual and Augmented Reality.

Academic qualifications

  • Accreditation to Supervise Research (HDR) defended at Aix-Marseille University, LIS on “Data extraction, analysis and visualization from images and videos sequences” – 2019
  • Doctor (PhD) defended at Aix-Marseille University, LIS on “Texture Analysis and Scientific Visualization” – 2001

Current Position


  • Co-editor of the Special Issue “The Mixed Reality Revolution: Challenges and Prospects“, Journal of Imaging – Deadline for manuscript submissions: 01 September 2021.
  • Co-editor of the Special Issue “Remote Sensing Image Fusion and Modeling“, Applied Sciences – Deadline for manuscript submissions: 20 December 2021.

A selection of recent relevant publications


Li, Y.; Mavromatis, S.; Zhang, F.; Du Z.; Sequeira, J.; Wang, Z.; Zhao X.; Liu R. Single-Image Super-Resolution for Remote Sensing Images Using a Deep Generative Adversarial Network With Local and Global Attention Mechanisms. IEEE Transactions on Geoscience and Remote Sensing 2021. https://doi.org/10.1109/TGRS.2021.3093043WOS – SJR(Q1)https://hal.archives-ouvertes.fr/hal-03309983

Jin, N.; Mavromatis, S.; Sequeira, J.; Curcio, S. A Robust Method of Eye Torsion Measurement for Medical Applications. MDPI Information, 11(9), 408, 2020. https://doi.org/10.3390/info11090408WOS – SJR (Q3) – OPEN ACCESShttps://hal-amu.archives-ouvertes.fr/hal-02919784

Chergui, A.; Ouchtati, S.; Mavromatis, S.; Bekhouche, S.E.; Sequeira, J.; and Dornaika, F. Kinship verification through facial images using multiscale and multilevel handcrafted features. SPIE Journal of Electronic Imaging 29(2), 2020. https://doi.org/10.1117/1.JEI.29.2.023017WOS – DBLP – SJR (Q3)https://hal-amu.archives-ouvertes.fr/hal-02531188

Qin, M.; Mavromatis, S.; Hu, L.; Zhang, F.; Liu, R.; Sequeira, J.; Du, Z. Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement. MDPI Remote Sensing 12(5), 758, 2020. https://doi.org/10.3390/rs12050758WOS – DBLP – SJR (Q1) – OPEN ACCESShttps://hal-amu.archives-ouvertes.fr/hal-02492404

Chergui, A.; Ouchtati, S.; Mavromatis, S.; Bekhouche, S.E.; Lashab, M.,; Sequeira, J. Kinship verification through facial images using CNN-based features. Traitement du Signal, Vol. 37, No. 1, pp. 1-8, 2019. https://doi.org/10.18280/ts.370101WOS – DBLP – SJR (Q3) – OPEN ACCESShttps://hal-amu.archives-ouvertes.fr/hal-02517794

Ouchtati, S.; Chergui, A.; Mavromatis, S.;Aissa, B.; Rafik, D.; Sequeira J. Novel method for brain tumor classification based on use of image entropy and seven Hu’s invariant moments. Traitement du Signal, Vol. 36, No. 6, pp. 483-491, 2019. https://doi.org/10.18280/ts.360602WOS – DBLP – SJR (Q3) – OPEN ACCESShttps://hal.archives-ouvertes.fr/hal-02400717

Li, X.; Meng, Q.; Li, W.; Zhang, C.; Jancso, T.; Mavromatis, S. An explorative study on the proximity of buildings to green spaces in urban areas using remotely sensed imagery. Annals of GIS 20.3, 2014https://doi.org/10.1080/19475683.2014.945482DBLP – SJR (Q2) – OPEN ACCESShttps://hal.archives-ouvertes.fr/hal-01286202

Li, X.; Meng, Q.; Gu X.; Jancso, T.; Yu, T.; Wang, K.; Mavromatis, S. A hybrid method combining pixel-based and object-oriented methods and its application in hungary using chinese hj-1 satellite images  International journal of remote sensing (IJRS), vol. 34, iss. 13, 2013. https://doi.org/10.1080/01431161.2013.780669WOS – SJR (Q1) – OPEN ACCESS – https://hal.archives-ouvertes.fr/hal-01286203


Monneau, A.; M’Sirdi, N.; Mavromatis S.; Varra G; Salesse M.; Sequeira J. Detection and Estimation of Helicopters Vibrations by Adaptive Notch Filters. Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Online Streaming, 2020. https://doi.org/10.5220/0009910302010207DBLPhttps://hal-amu.archives-ouvertes.fr/hal-02936935

Chergui, A.; Ouchtati, S.; Mavromatis, S.; Bekhouche S. E.; Sequeira J. Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images, Proceedings of the IEEE International Conference on Frontiers of Signal Processing (ICFSP), Marseille, France, 2019. https://www.doi.org/10.1109/ICFSP48124.2019.8938055IEEE Xplorehttps://hal.archives-ouvertes.fr/hal-02400686

Girard, R.; Mavromatis, S.; Sequeira, J.; Belanger, N.; Anoufa, G; A Vision-Based Assistance Key Differenciator for Helicopters Automonous Scalable Mission Althoefer K., Konstantinova J., Zhang K. (eds) Towards Autonomous Robotic Systems (TAROS) Lecture Notes in Computer Science, vol 11650. Springer, Cham. 2019. https://doi.org/10.1007/978-3-030-25332-5_18DBLP – SJR (Q2)https://hal-amu.archives-ouvertes.fr/hal-02095960

Chergui, A.; Ouchtati, S.; Mavromatis, S.; Bekhouche, S. E.; Sequeira, J. Kinship verification using mixed descriptors and multi block face representation IEEE international conference on networking and advanced systems (ICNAS), Algeria, 2019https://www.doi.org/10.1109/ICNAS.2019.8807875WOS – IEEE XPlorehttps://hal-amu.archives-ouvertes.fr/hal-02319850

Monneau, A.; M’Sirdi, N.; Mavromatis, S.; Varra, G.; Salesse, M.; Sequeira, J. Adaptive prediction for ship motion in rotorcraft maritime operations 5th CEAS conference on guidance, navigation and control (EUROGNC), Milan, Italy, 2019https://hal.archives-ouvertes.fr/hal-02095955

Huang, D.; Wang, Y.; Song, W.; Sequeira, J.; Mavromatis, S. Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition Schoeffmann K. et al. (eds) MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science, vol 10704. Springer, Cham. 2018. https://doi.org/10.1007/978-3-319-73603-7_37WOS – DBLP – SJR (Q2)https://hal.archives-ouvertes.fr/hal-01632263

Zoppitelli, P.; Mavromatis, S.; Sequeira, J.; Anoufa, G.; Belanger, N.; Fillias, F. Embedding intelligent image processing algorithms: the new safety enhancer for helicopters missions 44th European Rotorcraft Forum (ERF), Delft, Netherlands, 2018. https://hal.archives-ouvertes.fr/hal-02095941

Zoppitelli, P.; Mavromatis, S.; Sequeira J. Ellipse Detection in Very Noisy Environment 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS Association, 2017. https://hal-amu.archives-ouvertes.fr/hal-01569103

Jiahui Z.; Mavromatis, S.; Meng Q.; Sequeira, J.; Ying, Z. Using mathematical morphology on lidar data to extract information from urban vegetation IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China, 2016. https://doi.org/10.1109/IGARSS.2016.7729325IEEE Xplore – DBLPhttps://hal-amu.archives-ouvertes.fr/hal-01389854