Roberts Kadiķis is a senior researcher, head of Robotics and Machine Perception laboratory, and a chairman of the Scientific Council at EDI, where he works since 2011. The main research areas are 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.
Participation in projects
- Framework of key enabling technologies for safe and autonomous drones applications (COMP4DRONES) #H2020
- Vision, Identification, with Z-sensing Technology and key Applications (VIZTA) #H2020
- Artificial Intelligence for Digitizing Industry (AI4DI) #H2020
- Silicon IP Design House (SilHouse) #ESIF
- Programmable Systems for Intelligence in Automobiles (PRYSTINE) #H2020
- Cyber-physical systems, ontologies and biophotonics for safe&smart city and society (VPP SOPHIS) #SRP (VPP)
- Innovative technologies for acquisition and processing of biomedical images (InBiT) #ESIF
- Multifunkcionāla inteliģenta transporta sistēmas punkta tehnoloģija (MITS) #ESIF
The most important publications
- Buls, E., Kadikis, R., Cacurs, R., & Ārents, J. (2019, March). Generation of synthetic training data for object detection in piles. In Eleventh International Conference on Machine Vision (ICMV 2018) (Vol. 11041, p. 110411Z)
- Kadikis, R. (2018, April). Recurrent neural network based virtual detection line. In Tenth International Conference on Machine Vision (ICMV 2017) (Vol. 10696, p. 106961V). International Society for Optics and Photonics
- Dorbe, N., Jaundalders, A., Kadikis, R., & Nesenbergs, K. (2018). FCN and LSTM Based Computer Vision System for Recognition of Vehicle Type, License Plate Number, and Registration Country. Automatic Control and Computer Sciences, 52(2), 146-154.
- N. Dorbe, R. Kadikis, K. Nesenbergs. “Vehicle type and licence plate localisation and segmentation using FCN and LSTM”, Proceedings of New Challenges of Economic and Business Development 2017, Riga, Latvia, May 18-20, 2017, pp. 143-151
- Tamošiūnas, M., Jakovels, D., Rubins, U., Kadikis, R., Petrovska, R., & Šatkauskas, S. (2017, December). pEGFP transfection into murine skeletal muscle by electrosonoporation. In Biophotonics—Riga 2017 (Vol. 10592, p. 105920L). International Society for Optics and Photonics.
- Tamošiūnas, M., Kadikis, R., Saknīte, I., Baltušnikas, J., Kilikevičius, A., Lihachev, A., ... & Šatkauskas, S. (2016). Noninvasive optical diagnostics of enhanced green fluorescent protein expression in skeletal muscle for comparison of electroporation and sonoporation efficiencies. Journal of biomedical optics, 21(4), 045003.
- Kadikis, R. (2015, April). Registration method for multispectral skin images. In 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA) (pp. 232-235). IEEE.
- Jakovels, D., Saknite, I., Bliznuks, D., Spigulis, J., & Kadikis, R. (2015, May). Benign—A typical nevi discrimination using diffuse reflectance and fluorescence multispectral imaging system. In 2015 International Conference on BioPhotonics (BioPhotonics) (pp. 1-4). IEEE.
- Tamošiūnas, M., Jakovels, D., Ļihačovs, A., Kilikevičius, A., Baltušnikas, J., Kadikis, R., & Šatkauskas, S. (2014, October). Application of fluorescence spectroscopy and multispectral imaging for non-invasive estimation of GFP transfection efficiency. In Eighth International Conference on Advanced Optical Materials and Devices (AOMD-8) (Vol. 9421, p. 94210M). International Society for Optics and Photonics.
- Kadiķis, R., & Freivalds, K. (2013, December). Vehicle classification in video using virtual detection lines. In Sixth International Conference on Machine Vision (ICMV 2013) (Vol. 9067, p. 90670Y). International Society for Optics and Photonics.
- Kadiķis, R., & Freivalds, K. (2013). Efficient video processing method for traffic monitoring combining motion detection and background subtraction. In Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012) (pp. 131-141). Springer, India.
- Novickis, R., Levinskis, A., Kadiķis, R., Feščenko. V., Ozols, K. (2020). Functional architecture for autonomous driving and its implementation. 17th Biennial Baltic Electronics Conference (BEC2020), Tallinn, Estonia.