Roberts Kadiķis is a senior researcher and head of Robotics and Machine Perception laboratory at EDI, where he works since 2011. The main research areas are artificial intelligence, computer vision, and machine learning, while specific interests include deep neural networks (convolutional neural networks CNN, recurrent neural networks RNN, generative adversarial networks GAN), the learning of meaningful embeddings, explainable AI, generation of synthetic training data, bio-inspired learning systems, and computationally efficient vision algorithms. The applied experience of using deep models and other image processing tools includes the development of object detection, tracking, and image segmentation methods for tasks in transportation, mobility, biomedicine, and industry. The efficiency of the object detection algorithms was the main emphasis of the PhD thesis that was defended in the field of electronics at Riga Technical University in 2018.
Recent projects
- Efficient module for automatic detection of people and vehicles using video surveillance cameras (VAPI) #ERDF
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Intelligent Motion Control under Industry 4.E (IMOCO4.E) #H2020
- AI-improved organ on chip cultivation for personalised medicine (AImOOC) #H2020
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Augmenting and Evaluating the Physical and Digital Infrastructure for CCAM deployment (AUGMENTED CCAM) #Horizon Europe
- Histological recognition and analysis of veterinary tumors surgical margins by using artificial intelligence and multimodal imaging (HAVeT-AI ) #Horizon Europe
- Holographic microscopy- and artificial intelligence-based digital pathology for the next generation of cytology in veterinary medicine (VetCyto) #Horizon Europe
Recent publications
- Lulla M., Rutkovskis A., Slavinska A., Vilde A., Gromova A., Ivanovs M., Skadins A., Kadikis R., Elsts A. Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038
- Maksims Ivanovs, Roberts Kadiķis, Kaspars Ozols. 2021. “Perturbation-based methods for explaining deep neural networks: A survey” Elsevier B.V., https://doi.org/10.1016/j.patrec.2021.06.030
- Rihards Novickis, Aleksandrs Levinskis, Vitalijs Fescenko, Roberts Kadikis, Kaspars Ozols, Anna Ryabokon, Rupert Schorn, Jochen Koszescha, Selim Solmaz, Georg Stettinger, Akwasi Adu-Kyere, Lauri Halla-aho, Ethiopia Nigussie, Jouni Isoaho. "Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project", Appl. Sci. 2022, 12(1), 168;
- Romans Maliks, Roberts Kadikis. Multispectral Data Classification with Deep CNN for Plastic Bottle Sorting. 2021 6th International Conference on Mechanical Engineering and Robotics Research (ICMERR), 2022, pp. 58-65, ISBN: 978-1-6654-0641-3
- Ivanovs, Maksims, Kaspars Ozols, Artis Dobrajs, and Roberts Kadikis. 2022. "Improving Semantic Segmentation of Urban Scenes for Self-Driving Cars with Synthetic Images" Sensors 22, no. 6: 2252. https://doi.org/10.3390/s22062252
- Arents, J., Lesser, B., Bizuns, A., Kadikis, R., Buls, E., Greitans, M. (2022). Synthetic Data of Randomly Piled, Similar Objects for Deep Learning-Based Object Detection. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_59
- Mindaugas Tamošiūnas, Roberts Kadiķis, Mikus Melderis, Romāns Maļiks, Diāna Duplevska, Daira Viškere, Ilze Matīse-van Houtana, Blaž Cugmas. 2023. "Wide-field Raman spectral band imaging of tumor lesions in veterinary medicine" Translational Biophotonics: Diagnostics and Therapeutics III, 12627: pp. 295-302. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12627/1262737/Wide-field-Raman-spectral-band-imaging-of-tumor-lesions-in/10.1117/12.2686917.short
- Valērija Movčana, Arnis Strods, Karīna Narbute, Fēlikss Rūmnieks, Roberts Rimša, Gatis Mozoļevskis, Maksims Ivanovs, Roberts Kadiķis, Kārlis Gustavs Zviedris, Laura Leja, Anastasija Zujeva, Tamāra Laimiņa, Arturs Abols. "Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation" Data , 9(2), pp.28. https://doi.org/10.5281/zenodo.10203721
- Blaz Cugmas, Eva Štruc, Inese Bērziņa, Mindaugas Tamošiūnas, Laura Goldberga, Thierry Olivry, Kārlis Zviedris, Roberts Kadiķis, Maksims Ivanovs, Miran Bürmen, Peter Naglič. "Automated classification of pollens relevant to veterinary medicine" 2024 IEEE 14th International Conference Nanomaterials: Applications & Properties (NAP) https://ieeexplore.ieee.org/document/10739713