Tomas Kerepecky

Versatile Academic and Leader: M.Sc. Graduate, PhD Student, M.A. Candidate, AI Freelancer, and Non-Profit & Church Leader

Hello! I am deeply engaged in academia and leadership, with a focus on advanced technology, education, and spirituality. Following my M.Sc. in Computational Physics, I am now pursuing a PhD in Image Processing and Artificial Intelligence at the Czech Academy of Sciences, linked to CTU in Prague. Additionally, I am a freelance AI specialist, especially in Large Language Models, working to demystify and master these complex AI systems. Alongside, I am studying for an M.A. in Practical Theology, concentrating on Leadership at TCM International Institute, Austria, merging my tech skills with a thorough understanding of "E10 Leadership" (see below).

In 2022, I achieved a milestone with the Fulbright-Masaryk award, enabling research at Washington University in St. Louis, US. Beyond academia, I have held educational and leadership roles, including as executive director of a non-profit Christian outdoor organization from 2015 to 2021 (resumed from 2024), missionary leader, church planter and a visiting teacher at New Hope Mission School in Bihar, India.

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Education and Research

  • 2021-2022

    Washington University in St. Louis, Missouri, USA

    Fulbright-Masaryk Award

  • 2019-now

    Department of Mathematics, Czech Technical University in Prague, Czechia

    PhD student in Mathematical Engineering. Focusing on Image processing.

  • 2018-now

    TCM International Institute, Vienna, Austria.

    MA student in Practical Theology concentrated on Leadership studies.

  • 2015-2019

    Department of Physical Electronics, Czech Technical University in Prague, Czechia

    M.Sc. degree in Computational physics. Master thesis: Inverse Compton scattering by laser-accelerated electrons.

    PhD student in Computational physics till 2019.

Professional and Research Interests

Image Processing & Computer Vision

My focus is on the intersection of technology and visual data, developing systems to process...

My focus is on the intersection of technology and visual data, developing systems to process, analyze, and interpret images and videos. This field has applications in medical imaging, autonomous vehicles, and facial recognition, emphasizing the enhancement of image analysis accuracy and adaptability in real-world scenarios.

I am currently focusing on image restoration, object detection, face recognition, and object tracking.

Artificial Intelligence

I specialize in AI, aiming to replicate human intelligence in machines. My work covers...

I specialize in AI, aiming to replicate human intelligence in machines. My work covers deep learning in computer vision and natural language processing, focusing on algorithms that enable machines to learn, make decisions, and perform complex tasks. AI research drives innovation in healthcare, education, finance, and environmental management.

Prompt Engineering

As a prompt engineering specialist, I develop and optimize prompts for effective use of language models in...

As a prompt engineering specialist, I develop and optimize prompts for effective use of language models in various applications. This includes improving the capabilities of large language models in tasks like question answering and reasoning, ensuring their safety, and integrating them with external tools.

See more:

What is prompting?

E10 Leadership

My leadership philosophy is deeply rooted in the principles of servant leadership, a concept popularized by...�

My leadership philosophy is deeply rooted in the principles of servant leadership, a concept popularized by Robert Greenleaf but originally founded two millennia ago by Jesus of Nazareth, as described in the New Testament. This approach encourages us to Envision, Exemplify, Execute, Evaluate, and Enhance, as well as Engage, Encourage, Empower, Endorse, and Empathize with others. The E10 Leadership model embodies a commitment to serving first, from which leading naturally follows, aligning with Christian teachings and contemporary leadership practices.

I study servant leadership theoretically and also practically:

TCM International Institute Non-profit Atleti v Akci, z.s.

Multidisciplinary leader at the intersection of technology, AI, education, and theology, researching to innovate and inspire.

Experiential Education

My interest in experiential education is grounded in the principles of Kolb's Experiential Learning...

My interest in experiential education is grounded in the principles of Kolb's Experiential Learning Theory, which posits that learning is most effective through a cycle of experiencing, reflecting, thinking, and acting. This approach resonates with my belief in the transformative power of hands-on experiences and reflective observation for personal growth and development. In this context, learning is seen as a continuous, life-long process, emphasizing the importance of actively engaging with and reflecting on experiences to gain deeper insights and practical knowledge.

Publications

STAR: Screen Time and Actor Recognition in Video Content

2024 The German Conference on Pattern Recognition (GCPR)

Accurately measuring the duration of actors' presence in videos is a challenging task that goes beyond actor recognition. We propose the STAR pipeline, the new model designed to analyze the time performers appear on screen across diverse video content, including movies and TV shows. The proposed model has been successfully deployed and tested by the Czech TV infrastructure provider. Our pipeline uses machine learning techniques for shot detection, face detection, tracking, recognition, and introduces a novel shot-based method for calculating screen time. We present extensive experiments proving the robustness and real-time performance of our approach. Alongside the pipeline, we introduce the STAR dataset to address the need for high-quality benchmarks in evaluating screen time models, now available for download.

Paper accepted, link soon

NeRD: Neural field-based Demosaicking

2023 IEEE International Conference on Image Processing (ICIP)

We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns. Our approach leverages advancements in neural fields to perform demosaicking by representing an image as a coordinate-based neural network with sine activation functions. The inputs to the network are spatial coordinates and a low-resolution Bayer pattern, while the outputs are the corresponding RGB values. An encoder network, which is a blend of ResNet and U-net, enhances the implicit neural representation of the image to improve its quality and ensure spatial consistency through prior learning. Our experimental results demonstrate that NeRD outperforms traditional and state-of-the-art CNN-based methods and significantly closes the gap to transformer-based methods.

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Real-Time Wheel Detection and Rim Classification in Automotive Production

2023 IEEE International Conference on Image Processing (ICIP)

This paper proposes a novel approach to real-time automatic rim detection, classification, and inspection by combining traditional computer vision and deep learning techniques. At the end of every automotive assembly line, a quality control process is carried out to identify any potential defects in the produced cars. Common yet hazardous defects are related, for example, to incorrectly mounted rims. Routine inspections are mostly conducted by human workers that are negatively affected by factors such as fatigue or distraction. We have designed a new prototype to validate whether all four wheels on a single car match in size and type. Additionally, we present three comprehensive open-source databases, CWD1500, WHEEL22, and RB600, for wheel, rim, and bolt detection, as well as rim classification, which are free-to-use for scientific purposes.

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Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN

2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane. We address this issue by presenting Dual-Cycle, a new framework for joint deconvolution and fusion of dual-view fluorescence images. Inspired by the recent Neuroclear method, Dual-Cycle is designed as a cycle-consistent generative network trained in a self-supervised fashion by combining a dual-view generator and prior-guided degradation model. We validate Dual-Cycle on both synthetic and real data showing its state-of-the-art performance without any external training data.

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D3Net: Joint Demosaicking, Deblurring and Deringing

2020 IEEE International Conference on Pattern Recognition (ICPR)

Images acquired with standard digital cameras have Bayer patterns and suffer from lens blur. A demosaicking step is implemented in every digital camera, yet blur often remains unattended due to computational cost and instability of deblurring algorithms. Linear methods, which are computationally less demanding, produce ringing artifacts in deblurred images. Complex non-linear deblurring methods avoid artifacts, however their complexity imply offline application after camera demosaicking, which leads to sub-optimal performance. In this work, we propose a joint demosaicking deblurring and deringing network with a light-weight architecture inspired by the alternating direction method of multipliers. The proposed network has a transparent and clear interpretation compared to other black-box data driven approaches. We experimentally validate its superiority over state-of-the-art demosaicking methods with offline deblurring.

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Iterative Wiener Filtering for Deconvolution with Ringing Artifact Suppression

2019 27th European Signal Processing Conference (EUSIPCO)

Sensor and lens blur degrade images acquired by digital cameras. Simple and fast removal of blur using linear filtering, such as Wiener filter, produces results that are not acceptable in most of the cases due to ringing artifacts close to image borders and around edges in the image. More elaborate deconvolution methods with non-smooth regularization, such as total variation, provide superior performance with less artifacts, however at a price of increased computational cost. We consider the alternating directions method of multipliers, which is a popular choice to solve such non-smooth convex problems, and show that individual steps of the method can be decomposed to simple filtering and element-wise operations. Filtering is performed with two sets of filters, called restoration and update filters, which are learned for the given type of blur and noise level with two different learning methods. The proposed deconvolution algorithm is implemented in the spatial domain and can be easily extended to include other restoration tasks such as demosaicing and super-resolution. Experiments demonstrate performance of the algorithm with respect to the size of learned filters, number of iterations, noise level and type of blur.

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Inverse Compton scattering by laser-accelerated electrons

Czech Technical University in Prague. Computing and Information Centre, 2017

This thesis deals with the study of X- and -radiation during the interaction of relativistic electrons with the intense electromagnetic field. This mechanism is called inverse Compton scattering. For the purpose of examining the properties of radiation from inverse Compton scattering, a new COCO code has been implemented. Radiation spectrum is computed through the use of fast Fourier transform of the radiation field of electrons.

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Teaching and AI

Contact.

kerepecky@utia.cas.cz skype: live:kerepecky_1 +420 266 052 864
  • Filip Sroubek's Lab,
  • Department of Image Processing,
  • Institute of Information Theory and Automation,
  • Czech Academy of Sciences.