Pejman Rasti

Ph.D., Image Processing, Machine Learning

Dr. Pejman Rasti received his M.Sc. and Ph.D. degrees in image processing and machine learning at EMU, Cyprus, in (February) 2014, and the University of Tartu, Estonia (May) 2017 respectively. He has been working in the field of machine learning & image processing and is currently focusing in many research works related to the development of computer vision and machine learning algorithms for the purpose of characterization of plant growth. This includes segmentation, registration, classification, estimation ... with classical or machine learning based methods. He has been working as a post-doctoral researcher since July 2017 in Institut des sciences et techniques de l'ingénieur d'Angers (ISTIA) and is a member of the ImHorPhen research team.

Contact

Address
62, avenue Notre-Dame du Lac
Angers, 49000 France
Mobile Number
+33 769 36 61 64
Skype
Pejmanrasti
Email
pejman.rasti@univ-angers.fr

Expertise

Image Processing

Image Super-Resolution, Video Processing, Digital WaterMarking

Machine Learning

Biometric recognition and security

Artificial intelligence

Deep Learning

Artificial Materials

Liquid Lenses

Education

2014- 2017

Ph.D., Major in optic Image processing and Machine learning

University of Tartu

Tartu, Estonia

2012 - 2014

M.Sc, Major in Electrical and Electronic Engneering

Eastern Mediterranean University

Famagusta, Cyprus

2010 - 2013

B.Sc., Major in Electronic Engneering

Azad University

Isfahan, Iran

Teaching Experiences

Courses

Digital Image Processing-University of Tartu (2014 - 2016)

Data Analysis with Matlab-University of Tartu (2015 - 2017)

Seminars

Introduction to Image Super-Resolution - University of Lyon 1 (2016)

Artificial intelligence for bioinformatics - Bioinformatics summer school, University of Angers (2017)

Long-short term memory in deep learning - Deep Learning Day, University of Angers (2017)

Hands-on in TensorFlow - Deep Learning Day, University of Angers (2017)

Supervision and Mentorship

Graduate students

Tõnis Uiboupin - Super Resolution and Face Recognition Based People Activity Monitoring Enhancement Using Surveillance Camera (2016)

Undergraduate Students

Karl Tarvas - Edge Information Based Object Detection and Classifiation (2016)

Professional Duties

Editor & Reviwer

Reviewr of:
IEEE Transactions on Multimedia
Signal, Image and Video Processing
Optics Letters , ...

Editor of MJEE Journal

MemberShip

Vice-Chair of IEEE studet branch - University of Tartu (2015-2017)

IEEE (2012-Present)

Eurasip (2017-Present)

Awards, Grants & Honours

Long Term Scholarships

University of Tartu Scholarship (2014-2017)

Eastern Mediterranean University Scholarship (2012-2014)

Short Term Funds

Estonian DORA Plus fund - 2 times (2016-2017)

Estonian DORA T6 Fund (2015)

Estonian ICT Doctoral School Fund (2014)

Best Student Award at Azad University (2011)

Programing Skills

Matlab
92%
Python
75%
C++
60%
TensorFlow
75%

Publications

Journals

Rasti, Pejman, et al. "Reducible dictionaries for single image super-resolution based on patch matching and mean shifting." Journal of Electronic Imaging 26.2 (2017): 023024-023024.

Rasti, Pejman, et al. "Robust non-blind color video watermarking using QR decomposition and entropy analysis." Journal of Visual Communication and Image Representation 38 (2016): 838-847.

Rasti, Pejman, et al. "Dielectric elastomer stack actuator-based autofocus fluid lens." Applied optics 54.33 (2015): 9976-9980.

Conferences

Bujoreanu, D., Rasti, P., & Rousseau, D. (2017, August). On the value of graph-based segmentation for the analysis of structural networks in life sciences. In Signal Processing Conference (EUSIPCO), 2017 25th European (pp. 2664-2668). IEEE.

Rasti, Pejman, et al. "Improved interpolation kernels for super resolution algorithms." Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on. IEEE, 2016.

Loob, C., Rasti, P. (2017, May). Dominant and complementary multi-emotional facial expression recognition using c-support vector classification. In Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on (pp. 833-838). IEEE.

Journal And Conferences