Mahya M. Kashani

Mahya M. Kashani

I am a PhD fellow at ITU, Copenhagen, Danmark, I'm also Early Stage Researcher (ESR) within EU- REMARO project

Publications

You can also find my articles on my Google Scholar profile.

Reliable Plan Selection with Quantified Risk-Sensitivity

Published in NWPT 2023-34th Nordic Workshop on Programming Theory, 2023

This extended abstract paper is about reliable plan selection for marine robots.

Recommended citation: John, T., Kashani, M. M., Coffelt, J. P., Johnsen, E. B., & Wasowski, A. (2023). "Reliable Plan Selection with Quantified Risk-Sensitivity." In NWPT 2023-34th Nordic Workshop on Programming Theory. https://pure.itu.dk/da/publications/reliable-plan-selection-with-quantified-risk-sensitivity

Belief-based fault recovery for marine robotics

Published in The Eighth Joint Ontology Workshops (JOWO’22),'RobOntics 2022', 2022

This project entitled testing and statistical assessment of plans which is devoted to risk assessment and assistance for plan selection in underwater AI robotics.

Recommended citation: JP Coffelt, MM Kashani, A Wasowski, P Kampmann (2022). "The Eighth Joint Ontology Workshops (JOWO’22), August 15-19, 2022, Jönköping, Sweden, 1(3). https://ceur-ws.org/Vol-3249/paper3-RobOntics.pdf

Toward real-time image annotation using marginalized coupled dictionary learning

Published in Journal of Real-Time Image Processing, 2022

This journal paper is about a novel coupled dictionary learning approach is proposed to learn a limited number of visual prototypes and their corresponding semantics simultaneously. This approach leads to a real-time image annotation procedure.

Recommended citation: Roostaiyan, S. M., Hosseini, M. M., Kashani, M. M., & Amiri, S. H. (2022). " Toward real-time image annotation using marginalized coupled dictionary learning." Journal of Real-Time Image Processing, 19(3), 623-638. https://arxiv.org/abs/2304.06907

Scalable Image Annotation by Summarizing Training Samples into Labeled Prototypes

Published in The Quartely Journal of Signal and Data Processing, 2022

This quartely journal paper is about a novel approach to summarize training database (images and their relevant tags) into a small number of prototypes.

Recommended citation: Mohammadi Kashani, M., & Amiri, S. H. (2022). " Scalable Image Annotation by Summarizing Training Samples into Labeled Prototypes." Quarterly Journal of Signal and Data Processing, 18(4), 49-68.. http://jsdp.rcisp.ac.ir/files/site1/user_files_60a4f6/hamid_amiri-A-10-1891-1-1c4f635.pdf

Leveraging deep learning representation for search-based image annotation

Published in In 2017 Artificial Intelligence and Signal Processing Conference (AISP), 2017

This extended abstract paper is about integrate our feature extractors with 2PKNN (2 pass KNN) approach to obtain relevant tags of an image.

Recommended citation: Kashani, M. M., & Amiri, S. H. (2017, October). " Leveraging deep learning representation for search-based image annotation." In 2017 Artificial Intelligence and Signal Processing Conference (AISP) (pp. 156-161). IEEE. https://ieeexplore.ieee.org/abstract/document/8324073