K.Sudars has been working at the EDI since 2006. He has received PhD in Computer Science from University of Latvia, Faculty of Computing and he is co-author of 19 SCOPUS scientific publications focusing on R&D in signal processing, deep learning and computer vision. Currently his scientific interests are covering explainable AI, semantic image segmentation and object detection in images. Also K.Sudars is co-founder at start-up company WeedBot dedicated to AI based weeding for delicate crops.
Scopus Author ID 24512667900
Recent projects
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Automotive Intelligence for/at Connected Shared Mobility (AI4CSM) #H2020
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Artifical intelligence for more precise diagnostics (AI4DIAG) #ESIF
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Development of a robotic weed management equipment (RONIN) #ESIF
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Pētījums par datorredzes paņēmienu attīstību industrijas procesu norises automatizācijai (DIPA) #ESIF
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Cyber-physical systems, ontologies and biophotonics for safe&smart city and society (VPP SOPHIS) #SRP (VPP)
- Cyber-physical systems, ontologies and biophotonics for safe&smart city and society (GUDPILS) #SRP (VPP)
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Smart non-contact phenotyping of raspberries and quinces using machine learning methods, hyperspectral and 3D images (AKFen) #SRP (VPP)
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Programmable Systems for Intelligence in Automobiles (PRYSTINE) #H2020
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A Deep Learning Approach for Osteoporosis Identification using Cone-beam Computed Tomography (OSTAK) #H2020
- New technology to produce hydrogen from Renewable Energy Sources based on AI with optimized costs for environmental applications (HydroG(re)EnergY-Env) #H2020
Recent publications
- Sudars, K., Jasko, J., Namatevs I., Ozola L., Badaukis, N. (2020). Dataset of annotated food crops and weed images for robotic computer vision control, Data in Brief, 31. doi:10.1016/j.dib.2020.105833
- Namatevs, I., Sudars, K., Polaka, I., Automatic data labeling by neural networks for the counting of objects in videos, Procedia Computer Science, Vol.149, pp. 151-158, 2019
- Sudars, Kaspars, Ivars Namatēvs, and Kaspars Ozols. 2022. "Improving Performance of the PRYSTINE Traffic Sign Classification by Using a Perturbation-Based Explainability Approach" Journal of Imaging 8, no. 2: 30. https://doi.org/10.3390/jimaging8020030