Polina Kurtser
Title: Researcher School/office: School of Science and Technology
Email:
Phone: +46 19 301410
Room: T1213
Research subject
Research environments
About Polina Kurtser
I am a post-doctoral researcher at Mobile Robotics and Olfaction Lab (MR&O) at Örebro University. Before to joining the MR&O lab in April 2019, I completed my PhD at the Department of Industrial Engineering and Management, Ben-Gurion University of the Negev (BGU) in Beer-Sheva, Israel. I have a Bachelors degree in Biomedical Engineering and a Masters degree in Industrial Engineering and Management from Ben-Gurion University of the Negev. I’ve been a short term visiting scholar at Forschungszentrum Jülich, Germany (2013) and Umeå University, Sweden (2016).
My research interests include computer vision, statistical analysis and machine learning techniques for agricultural robotic and medical imaging applications. I currently mainly focus on dynamic sensing, and information content estimation.
In my PhD I’ve been involved the EU Horizon 2020 project SWEEPER. The resulted product:
My list of publications is available on Google scholar page.
Research teams
Publications
Articles in journals |
Conference papers |
Articles in journals
Herck, L. v. , Kurtser, P. , Wittemans, L.
& Edan, Y. (2020). Crop design for improved robotic harvesting: A case study of sweet pepper harvesting. Biosystems Engineering, 192, 294-308.
Arad, B. , Balendonck, J. , Barth, R. , Ben-Shahar, O. , Edan, Y. , Hellström, T. , Hemming, J. , Kurtser, P.
& et al.
(2020). Development of a sweet pepper harvesting robot. Journal of Field Robotics.
Kurtser, P. , Ringdahl, O. , Rotstein, N. , Berenstein, R.
& Edan, Y. (2020). In-field grape cluster size assessment for vine yield estimation using a mobile robot and a consumer level RGB-D camera. IEEE Robotics and Automation Letters, 5 (2), 2031-2038.
Kurtser, P.
& Edan, Y. (2020). Planning the sequence of tasks for harvesting robots. Robotics and Autonomous Systems, 131.
Zemmour, E. , Kurtser, P.
& Edan, Y. (2019). Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection. Sensors, 19 (9).
Arad, B. , Kurtser, P. , Barnea, E. , Harel, B. , Edan, Y.
& Ben-Shahar, O. (2019). Controlled Lighting and Illumination-Independent Target Detection for Real-Time Cost-Efficient Applications. The Case Study of Sweet Pepper Robotic Harvesting. Sensors, 19 (6).
Ringdahl, O. , Kurtser, P.
& Edan, Y. (2019). Evaluation of approach strategies for harvesting robots: Case study of sweet pepper harvesting. Journal of Intelligent and Robotic Systems, 95 (1), 149-164.
Levi-Bliech, M. , Kurtser, P. , Pliskin, N.
& Fink, L. (2019). Mobile apps and employee behavior: An empirical investigation of the implementation of a fleet-management app. International Journal of Information Management, 49, 355-365.
Kurtser, P.
& Edan, Y. (2018). Statistical models for fruit detectability: spatial and temporal analyses of sweet peppers. Biosystems Engineering, 171, 272-289.
Kurtser, P.
& Edan, Y. (2018). The use of dynamic sensing strategies to improve detection for a pepper harvesting robot. IEEE International Conference on Intelligent Robots and Systems. Proceedings, 8286-8293.
Conference papers
Kurtser, P. , Ringdahl, O. , Rotstein, N.
& Andreasson, H. (2020). PointNet and geometric reasoning for detection of grape vines from single frame RGB-D data in outdoor conditions.
In:
Proceedings of the Northern Lights Deep Learning Workshop. Paper presented at 3rd Northern Lights Deep Learning Workshop, Tromsö, Norway 20-21 January, 2019 (pp. 1-6). NLDL.
Kurtser, P. , Hanell, U.
& Andreasson, H. (2020). Robotic Platform for Precise Mechanical Stress Induction in Greenhouses Cultivation.
In:
2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). Paper presented at IEEE 16th International Conference on Automation Science and Engineering (CASE), (Online Zoom Meeting), Hong Kong, August 20-21, 2020 (pp. 1558-1565). IEEE.
Ringdahl, O. , Kurtser, P.
& Edan, Y. (2019). Performance of RGB-D camera for different object types in greenhouse conditions.
In:
Libor Přeučil, Sven Behnke, Miroslav Kulich, 2019 European Conference on Mobile Robots (ECMR). Paper presented at 2019 European Conference on Mobile Robots (ECMR), Prague, Czech Republic, September 4-6, 2019 (pp. 1-6). IEEE.
Levi-Bliech, M. , Kurtser, P. , Pliskin, N.
& Fink, L. (2018). The effects of a fleet-management app on driver behavior.
Paper presented at 26th European Conference on Information Systems (ECIS2018), Portsmouth, UK, June 26-28, 2018..
Zemmour, E. , Kurtser, P.
& Edan, Y. (2017). Dynamic thresholding algorithm for robotic apple detection.
In:
2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). Paper presented at IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2017), Coimbra, Portugal- April 26-28, 2017 (pp. 240-246). IEEE.
Ringdahl, O. , Kurtser, P.
& Edan, Y. (2017). Strategies for selecting best approach direction for a sweet-pepper harvesting robot.
In:
Yang Gao, Saber Fallah, Yaochu Jin, Constantina Lekakou, Towards Autonomous Robotic Systems (Taros 2017). Paper presented at TAROS 2017: the 18th Towards Autonomous Robotic Systems (TAROS) Conference, University of Surrey, Guildford, UK, July 19–21, 2017 (pp. 516-525). Cham: Springer.
Ringdahl, O. , Kurtser, P. , Barth, R.
& Edan, Y. (2016). Operational flow of an autonomous sweetpepper harvesting robot.
Paper presented at The 5th Israeli Conference on Robotics 2016, Air Force Conference Center Hertzilya, Israel, 13-14 April, 2016.
Kurtser, P. , Arad, B. , Ben-Shahar, O. , van Bree, M. , Moonen, J. , van Tujil, B.
& Edan, Y. (2016). Robotic data acquisition of sweet pepper images for research and development.
Paper presented at The 5th Israeli Conference on Robotics 2016, Air Force Conference Center Hertzilya, Israel, 13-14 April, 2016.
Harel, B. , Kurtser, P. , van Herck, L. , Parmet, Y.
& Edan, Y. (2016). Sweet pepper maturity evaluation via multiple viewpoints color analyses.
Paper presented at CIGR-AgEng Conference, Aarhus, Denmark, 26-29 June, 2016 (pp. 1-7).
Kurtser, P. , Levi, O.
& Gontar, V. (2012). Detection and classification of ECG chaotic components using ANN trained by specially simulated data.
In:
Jayne, C; Yue, S; Iliadis, L, Engineering Applications of Neural Networks. Paper presented at 13th International Conference on Engineering Applications of Neural Networks (EANN 2012), London, UK, September 20-23, 2012 (pp. 193-202). Berlin, Heidelberg: Springer.