Biomedical Imaging Algorithms

Biomedical Imaging Algorithms Research Group

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We’re a group researching in the areas of humanoid, cognitive developmental, neuro-, and collaborative robotics. Robots with artificial electronic skins are one of our specialties.

In our research, we employ the so-called synthetic methodology, or “understanding by building”, with two main goals:

  1. Understanding cognition and its development. In particular, we’re interested in the “body in the brain“: how do babies learn to represent their bodies and the space around it (peripersonal space) and what are the mechanisms in the brain. We build embodied computational models on humanoid robots to uncover how these representations operate.
  2. Robots safe and natural around humans. Taking inspiration from humans, we make robots exploit multimodal information (mostly vision and touch) to share space with humans. We’re interested in physical and social human-robot interaction.

For more details about our Research see the corresponding tab.

Our full affiliation is Vision for Robotics and Autonomous Systems, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague.

Humanoids group

Matej Hoffmann (Assistant Professor, coordinator)
Google Scholar profile
Tomas Svoboda (Associate Professor)
Google Scholar profile
Karla Stepanova (Postdoc)
Google Scholar profile
Zdenek Straka (PhD Student)
Google Scholar profile
Petr Svarny (PhD Student)
Google Scholar profile
Filipe Gama (PhD Student)
Shubhan Patni (PhD Student)


For our publications, please see the Google Scholar profiles of individual group members. For demos see our YouTube channel.
More information can also be found here.

Models of body representations

How do babies learn about their bodies? Newborns probably do not have a holistic perception of their body; instead they are starting to pick up correlations in the streams of individual sensory modalities (in particular visual, tactile, proprioceptive). The structure in these streams allows them to learn the first models of their bodies. The mechanisms behind these processes are largely unclear. In collaboration with developmental and cognitive psychologists, we want to shed more light on this topic by developing robotic models.

Safe physical human-robot interaction

Robots are leaving the factory, entering domains that are far less structured and starting to share living spaces with humans. As a consequence, they need to dynamically adapt to unpredictable interactions with people and guarantee safety at every moment. “Body awareness” acquired through artificial skin can be used not only to improve reactions to collisions, but when coupled with vision, it can be extended to a surface around the body (so-called peripersonal space), facilitating collision avoidance and contact anticipation, eventually leading to safer and more natural interaction of the robot with objects, including humans.

Automatic robot self-calibration

Standard robot calibration procedures require prior knowledge of a number of quantities from the robot’s environment. These conditions have to be present for recalibration to be performed. This has motivated alternative solutions to the self-calibration problem that are more “self-contained” and can be performed automatically by the robot. These typically rely on self-observation of specific points on the robot using the robot’s own camera(s). The advent of robotic skin technologies opens up the possibility of completely new approaches. In particular, the kinematic chain can be closed and the necessary redundant information obtained through self-touch, broadening the sample collection from end-effector to whole body surface. Furthermore, the possibility of truly multimodal calibration – using visual, proprioceptive, tactile, and inertial information – is open.

  • whatever…

An increasing role of AI algorithms in search for effective cancer treatments

Artificial intelligence algorithms of scientists from the Faculty of Electrical Engineering of CTU and the Faculty of Medicine of the University of Olomouc help to accelerate the development of anticancer drugs. Artificial intelligence algorithms will play an increasing role in the search for effective cancer treatments. Machine learning methods can significantly reduce the time, effort …read more

IBM Great Minds

IBM, as every year, opens the prestigious Great Minds internships at IBM’s laboratories in Zürich, Johannesburg and Nairobi To apply and for more information about the cooperation, please contact Jan Louda ( Applications are accepted until March  4, 2019. Who interested in biomedical applications may apply for a recommendation to prof. Jan Kybic

Interview with prof. Chang-hee (Andy) Won, Ph.D.

From February to July 2019, prof. Chang-hee Won of Temple University in Pennsylvania, USA will be visiting CTU as a holder of the Fulbright-CTU Distinguished Chair fellowship. His professional interests include sensors and image processing and advanced control theory. His host will be prof. Jan Kybic from the Department of Cybernetics of the Faculty of …read more

Ing. Jiří Borovec defended his Ph.D. thesis

Ing. Jiří Borovec successfully defended his Ph.D. thesis entitled Analysis of microscopy images (supervisor: prof. Dr. Ing. Jan Kybic). Congratulations!

Miguel Amável dos Santos Pinheiro defended his Ph.D. thesis

Miguel Amável dos Santos Pinheiro successfully defended his Ph.D. thesis entitled Graph and Point Cloud Matching for Image Registration (supervisor: Prof. Jan Kybic). Congratulations!

Best paper award for Jiří Borovec and Jan Kybic

Paper “Binary pattern dictionary learning for gene expression representation in drosophila imaginal discs” by Jiří Borovec and Jan Kybic received Best Paper Award at MCBMIIA 2016. Congratulations!

Results of our BIA research group presented in Technicall magazine

The results of the Biomedical Imaging Algorithms (BIA) research group headed by doc. Jan Kybic are presented in Technicall magazine (1/2014). To learn more, see the articles (in Czech) called Počítač v roli patologa and Užitečný nástroj pro genetiky.

Juan David Garcia-Arteaga defended his Ph.D. thesis

Juan David Garcia-Arteaga successfully defended his Ph.D. thesis entitled Multichannel Image Information Similarity Measures: Applications to Colposcopy Image Registrations (supervisor: doc. Jan Kybic). Congratulations!
Aleš Král: Methods for Hierarchical Classification of Histopathology Images (PDF) Kybic Jan 2022 OI ZUI
Markéta Kvašová: 3D carotid artery reconstruction from 2D in-vitro ultrasound images (PDF) Kybic Jan 2022
Petr Novota: Rain Intensity Estimation from CML Link Data (PDF) Kybic Jan 2022 KyR
Vojtěch Brejtr: Histological image registration using optical flow estimation and deep learning (PDF) Kybic Jan 2022
Artem Moroz: Prediction of Atherosclerotic Plaque Parameters from In-Vivo Ultrasound Carotid Artery Images (PDF) Kybic Jan 2022 KyR
Adam Herold: Application of Machine Learning for the Higgs Boson Mass Reconstruction Using ATLAS Data (PDF) Kybic Jan 2022 KyR
Radka Olyšarová: Automatic Estimation of hdEEG Electrode Positions (PDF) Kybic Jan 2021 OI ZUI
Pavlína Koutecká: Automatic Detection of Metastases in Whole-Slide Lymph Node Images Using Deep Neural Networks (PDF) Kybic Jan 2020 KyR
Vojtěch Poříz: Diabetic Retinopathy Detection Using Neural Networks (PDF) Kybic Jan 2020 KyR
David Kunz: Robust and Fast Local All Pass Image Registration (PDF) Kybic Jan 2020 OI ICS
Jakub Malý: Automatic event recognition for Higgs boson detection (PDF) Kybic Jan 2020 KyR KyR
Josef Grus: Gel Electrophoresis Image Analysis (PDF) Kybic Jan 2020 KyR
Branislav Doubek: Implementation of Multiple Instance Learning using Markov Networks (PDF) Kybic Jan 2020
Petr Kočiš: Vessel Detection and Red Blood Cells Velocity Algorithm for Microcirculation Analysis (PDF) Kybic Jan 2019 BMII BMI
Latnerová Iva: Reduction of False Positives in Lung Nodule Detection Algorithm (PDF) Kybic Jan 2014 BMII BMI
Responsible person: Jan Kybic