Mateusz Wąsala

2026

1. Automation

   Vision-based automatic fruit counting with UAV

   Hubert Szolc, Mateusz Wasala, Remigiusz Mietla, Kacper Iwicki, and Tomasz Kryjak

   In Automation 2025: recent advances in automation, robotics and measurement techniques, Sep 2026

   Abstract DOI arXiv Bib

   The use of unmanned aerial vehicles (UAVs) for smart agriculture is becoming increasingly popular. This is evidenced by recent scientific works, as well as the various competitions organised on this topic. Therefore, in this work we present a system for automatic fruit counting using UAVs. To detect them, our solution uses a vision algorithm that processes streams from an RGB camera and a depth sensor using classical image operations. Our system also allows the planning and execution of flight trajectories, taking into account the minimisation of flight time and distance covered. We tested the proposed solution in simulation and obtained an average score of 87.27/100 points from a total of 500 missions. We also submitted it to the UAV Competition organised as part of the ICUAS 2024 conference, where we achieved an average score of 84.83/100 points, placing 6th in a field of 23 teams and advancing to the finals.

   @inproceedings{badap-agh:165951,
     title = {Vision-based automatic fruit counting with UAV},
     author = {Szolc, Hubert and Wasala, Mateusz and Mietla, Remigiusz and Iwicki, Kacper and Kryjak, Tomasz},
     year = {2026},
     series = {Automation~2025: recent advances in automation, robotics and measurement techniques},
     pages = {243–252},
     doi = {10.1007/978-3-032-08359-3_23},
     collection = {Automation~2025: recent advances in automation, robotics and measurement techniques},
   }

2025

1. SPA

   Embedded perception and control system for an autonomous surface vehicle using visual-LiDAR fusion and SLAM

   Olivier Lichota, Mateusz Wasala, Hubert Szolc, and Tomasz Kryjak

   In SPA 2025 : Signal Processing - algorithms, architectures, arrangements, and applications : Poznan, 17th–19th September 2025 : conference proceedings, Sep 2025

   Abstract DOI Bib

   The paper presents the design and implementation of a modular perception and control system for an autonomous surface vehicle (ASV), developed on a System-on-Module (SoM) embedded platform. The system was created with the goal of participating in the international RoboBoat competition. The high-level architecture integrates data from a stereo camera, LiDAR sensor, and a GNSS unit with an Inertial Measurement Unit (IMU), enabling object detection, simultaneous localization and mapping (SLAM), and autonomous navigation. We evaluated the use of a quantized convolutional neural network (YOLOv11) for object classification, obstacle detection based on LiDAR data, and control module operation using NAV2 within the ROS 2 environment under limited computational resources. Accelerating visual detection algorithms with CUDA and TensorRT libraries on the onboard NVIDIA Jetson Orin Nano platform enabled effective real-time and low-energy classification. Tests carried out in simulation demonstrated the correct operation of the perception and control system. The average runtime of the system using the quantised YOLO model with GPU was 26.01 ms. This equates to an approximate speed increase of 6.7 times compared to inference performed on an ARM processor, and an approximate speed increase of 5.5 times compared to a standard CPU.

   @inproceedings{badap-agh:163124,
     title = {Embedded perception and control system for an autonomous surface vehicle using visual-LiDAR fusion and SLAM},
     author = {Lichota, Olivier and Wasala, Mateusz and Szolc, Hubert and Kryjak, Tomasz},
     year = {2025},
     series = {SPA~2025 [Dokument elektroniczny] : Signal Processing - algorithms, architectures, arrangements, and applications : Poznan, 17th–19th September~2025 : conference proceedings},
     pages = {153–158},
     doi = {10.23919/SPA65537.2025.11215136},
     collection = {SPA~2025 [Dokument elektroniczny] : Signal Processing - algorithms, architectures, arrangements, and applications : Poznan, 17th–19th September~2025 : conference proceedings},
   }

2. DSD

   Hardware-aware feature extraction quantisation for real-time visual odometry on FPGA platforms

   Mateusz Wasala, Mateusz Smolarczyk, Michal Danilowicz, and Tomasz Kryjak

   In DSD 2025 : 28th euromicro conference on Digital System Design : 10–12 September 2025, Salerno, Italy, Sep 2025

   Abstract DOI arXiv Bib

   Accurate position estimation is essential for modern navigation systems deployed in autonomous platforms, including ground vehicles, marine vessels, and aerial drones. In this context, Visual Simultaneous Localisation and Mapping (VSLAM) which includes Visual Odometry - relies heavily on the reliable extraction of salient feature points from the visual input data. In this work, we propose an embedded implementation of an unsupervised architecture capable of detecting and describing feature points. It is based on a quantised SuperPoint convolutional neural network. Our objective is to minimise the computational demands of the model while preserving high detection quality, thus facilitating efficient deployment on platforms with limited resources, such as mobile or embedded systems. We implemented the solution on an FPGA System-on-Chip (SoC) platform, specifically the AMD/Xilinx Zynq UltraScale+, where we evaluated the performance of Deep Learning Processing Units (DPUs) and we also used the Brevitas library and the FINN framework to perform model quantisation and hardware-aware optimisation. This allowed us to process 640 × 480 pixel images at up to 54 fps on an FPGA platform, outperforming state-of-the-art solutions in the field. We conducted experiments on the TUM dataset to demonstrate and discuss the impact of different quantisation techniques on the accuracy and performance of the model in a visual odometry task.

   @inproceedings{badap-agh:165139,
     title = {Hardware-aware feature extraction quantisation for real-time visual odometry on FPGA platforms},
     author = {Wasala, Mateusz and Smolarczyk, Mateusz and Danilowicz, Michal and Kryjak, Tomasz},
     year = {2025},
     series = {DSD~2025 [Dokument elektroniczny] : 28th euromicro conference on Digital System Design : 10–12 September~2025, Salerno, Italy},
     pages = {539–548},
     doi = {10.1109/DSD67783.2025.00080},
     collection = {DSD~2025 [Dokument elektroniczny] : 28th euromicro conference on Digital System Design : 10–12 September~2025, Salerno, Italy},
   }

2023

1. MMAR

   Detection-segmentation convolutional neural network for autonomous vehicle perception

   Maciej Baczmanski, Robert Synoczek, Mateusz Wasala, and Tomasz Kryjak

   In MMAR 2023 : 27th international conference on Methods and Models in Automation and Robotics : 22–25 August 2023, Międzyzdroje, Poland : on line proceedings : technical papers, Sep 2023

   Abstract DOI arXiv Bib

   Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-performance computing platforms. In the case of autonomous vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms with limited computing power, which makes it difficult to meet the requirements described above. A reduction in the complexity of the network can be achieved by using an appropriate: architecture, representation (reduced numerical precision, quantisation, pruning), and computing platform. In this paper, we focus on the first factor – the use of so-called detection-segmentation networks as a component of a perception system. We considered the task of segmenting the drivable area and road markings in combination with the detection of selected objects (pedestrians, traffic lights, and obstacles). We compared the performance of three different architectures described in the literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments on a custom dataset consisting of approximately 500 images of the drivable area and lane markings, and 250 images of detected objects. Of the three methods analysed, MultiTask V3 proved to be the best, achieving 99% mAP50for detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane segmentation, as well as 124 fps on the RTX 3060 graphics card. This architecture is a good solution for embedded perception systems for autonomous vehicles. The code is available at: https://github.com/vision-agh/MMAR_2023.

   @inproceedings{badap-agh:148543,
     title = {Detection-segmentation convolutional neural network for autonomous vehicle perception},
     author = {Baczmanski, Maciej and Synoczek, Robert and Wasala, Mateusz and Kryjak, Tomasz},
     year = {2023},
     series = {MMAR~2023 [Dokument elektroniczny] : 27th~international conference on Methods and Models in Automation and Robotics : 22–25 August 2023, Mi\k{e}dzyzdroje, Poland : on line proceedings : technical papers},
     pages = {117–122},
     doi = {10.1109/MMAR58394.2023.10242398},
     collection = {MMAR~2023 [Dokument elektroniczny] : 27th~international conference on Methods and Models in Automation and Robotics : 22–25 August 2023, Mi\k{e}dzyzdroje, Poland : on line proceedings : technical papers},
   }

2. ARC

   Implementation of a perception system for autonomous vehicles using a detection-segmentation network in SoC FPGA

   Maciej Baczmanski, Mateusz Wasala, and Tomasz Kryjak

   In Applied Reconfigurable Computing : architectures, tools, and applications : 19th international symposium, ARC 2023 : Cottbus, Germany, September 27-29, 2023 : proceedings, Sep 2023

   Abstract DOI arXiv Bib

   Perception and control systems for autonomous vehicles are an active area of scientific and industrial research. These solutions should be characterised by high efficiency in recognising obstacles and other environmental elements in different road conditions, real-time capability, and energy efficiency. Achieving such functionality requires an appropriate algorithm and a suitable computing platform. In this paper, we have used the MultiTaskV3 detection-segmentation network as the basis for a perception system that can perform both functionalities within a single architecture. It was appropriately trained, quantised, and implemented on the AMD Xilinx Kria KV260 Vision AI embedded platform. By using this device, it was possible to parallelise and accelerate the computations. Furthermore, the whole system consumes relatively little power compared to a CPU-based implementation (an average of 5 watts, compared to the minimum of 55 watts for weaker CPUs, and the small size (119mm x 140mm x 36mm) of the platform allows it to be used in devices where the amount of space available is limited. It also achieves an accuracy higher than 97% of the mAP (mean average precision) for object detection and above 90% of the mIoU (mean intersection over union) for image segmentation. The article also details the design of the Mecanum wheel vehicle, which was used to test the proposed solution in a mock-up city.

   @inproceedings{badap-agh:149007,
     title = {Implementation of a perception system for autonomous vehicles using a detection-segmentation network in SoC FPGA},
     author = {Baczmanski, Maciej and Wasala, Mateusz and Kryjak, Tomasz},
     year = {2023},
     series = {Applied Reconfigurable Computing : architectures, tools, and applications : 19th~international symposium, ARC~2023 : Cottbus, Germany, September 27-29, 2023 : proceedings},
     pages = {200–211},
     doi = {10.1007/978-3-031-42921-7_14},
     collection = {Applied Reconfigurable Computing : architectures, tools, and applications : 19th~international symposium, ARC~2023 : Cottbus, Germany, September 27-29, 2023 : proceedings},
   }

2022

1. Electronics

   An efficient real-time FPGA-based ORB feature extraction for an UHD video stream for embedded visual SLAM

   Mateusz Wasala, Hubert Szolc, and Tomasz Kryjak

   Electronics, Sep 2022

   Abstract DOI Bib

   The detection and description of feature points are important components of many computer vision systems. For example, in the field of autonomous unmanned aerial vehicles (UAV), these methods form the basis of so-called Visual Odometry (VO) and Simultaneous Localisation and Mapping (SLAM) algorithms. In this paper, we present a hardware feature points detection system able to process a 4K video stream in real-time. We use the ORB algorithm—Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features)—to detect and describe feature points in the images. We make numerous modifications to the original ORB algorithm (among others, we use the RS-BRIEF instead of classic R-BRIEF) to adapt it to the high video resolution, make it computationally efficient, reduce the resource utilisation and achieve lower power consumption. Our hardware implementation supports a 4 ppc (pixels per clock) format (with simple adaptation to 2 ppc, 8 ppc, and more) and real-time processing of a 4K video stream (UHD—Ultra High Definition, 3840×2160 pixels) @ 60 frames per second (150 MHz clock). We verify our system using simulations in the Vivado IDE and implement it in hardware on the ZCU 104 evaluation board with the AMD Xilinx Zynq UltraScale+ MPSoC device. The proposed design consumes only 5 watts.

   @article{badap-agh:141501,
     title = {An efficient real-time FPGA-based ORB feature extraction for an UHD video stream for embedded visual SLAM},
     author = {Wasala, Mateusz and Szolc, Hubert and Kryjak, Tomasz},
     year = {2022},
     journal = {Electronics},
     series = {Electronics},
     volume = {11},
     number = {14},
     pages = {2259},
     doi = {10.3390/electronics11142259},
     issn = {2079-9292},
     collection = {Electronics},
   }

2. JSPS

   Automotive perception system evaluation with reference data from a UAV’s camera using ArUco markers and DCNN

   Krzysztof Blachut, Michal Danilowicz, Hubert Szolc, Mateusz Wasala, Tomasz Kryjak, and 1 more author

   Journal of Signal Processing Systems for Signal, Image, and Video Technology, Sep 2022

   Abstract DOI Bib

   Testing and evaluation of an automotive perception system is a complicated task which requires special equipment and infrastructure. To compute key performance indicators and compare the results with real-world situation, some additional sensors and manual data labelling are often required. In this article, we propose a different approach, which is based on a UAV equipped with a 4K camera flying above a test track. Two computer vision methods are used to precisely determine the positions of the objects around the car – one based on ArUco markers and the other on a DCNN (we provide the algorithms used on GitHub). The detections are then correlated with the perception system readings. For the static and dynamic experiments, the differences between various systems are mostly below 0.5 m. The results of the experiments performed indicate that this approach could be an interesting alternative to existing evaluation solutions.

   @article{badap-agh:141200,
     title = {Automotive perception system evaluation with reference data from a UAV's camera using ArUco markers and DCNN},
     author = {Blachut, Krzysztof and Danilowicz, Michal and Szolc, Hubert and Wasala, Mateusz and Kryjak, Tomasz and Komorkiewicz, Mateusz},
     year = {2022},
     journal = {Journal of Signal Processing Systems for Signal, Image, and Video Technology},
     series = {Journal of Signal Processing Systems for Signal, Image, and Video Technology},
     volume = {94},
     number = {7},
     pages = {675},
     doi = {10.1007/s11265-021-01734-3},
     issn = {1939-8018},
     collection = {Journal of Signal Processing Systems for Signal, Image, and Video Technology},
   }

3. Electronics Sonata
   

   Real-time CLAHE algorithm implementation in SoC FPGA device for 4K UHD video stream

   Tomasz Kryjak, Krzysztof Blachut, Hubert Szolc, and Mateusz Wasala

   Electronics, Sep 2022

   Abstract DOI Bib

   One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm—is implemented in hardware. An FPGA platform is used for this purpose due to the ability to run parallel computations and very low power consumption. To enable the processing of a 4K resolution (UHD, 3840 × 2160 pixels) video stream at 60 fps (frames per second) by using the CLAHE method, it is necessary to use a vector data format and process multiple pixels simultaneously. The algorithm realised in this work can be a component of a larger vision system, such as in autonomous vehicles or drones, but it can also support the analysis of underwater, thermal, or medical images both by humans and in an automated system.

   @article{badap-agh:141231,
     title = {Real-time CLAHE algorithm implementation in SoC FPGA device for 4K UHD video stream},
     projects = {Sonata},
     author = {Kryjak, Tomasz and Blachut, Krzysztof and Szolc, Hubert and Wasala, Mateusz},
     year = {2022},
     journal = {Electronics},
     series = {Electronics},
     volume = {11},
     number = {14},
     pages = {2248},
     doi = {10.3390/electronics11142248},
     issn = {2079-9292},
     collection = {Electronics},
   }

4. MECO Sonata
   

   Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream

   Mateusz Wasala and Tomasz Kryjak

   In 11th Mediterranean Conference on Embedded Computing (MECO) : 7–10 June 2022, Budva, Montenegro, Sep 2022

   Abstract DOI arXiv Bib

   Object detection is an essential component of many vision systems. For example, pedestrian detection is used in advanced driver assistance systems (ADAS) and advanced video surveillance systems (AVSS). Currently, most detectors (e.g. the YOLO - You Only Look Once - family) use deep convolutional neural networks. However, due to their high computational complexity, they are not able to process a very high-resolution video stream in real-time, especially within a limited energy budget. In this paper we present a hardware implementation of the well-known pedestrian detector with HOG (Histogram of Oriented Gradients) feature extraction and SVM (Support Vector Machine) classification. Our system running onAMD Xilinx Zynq UltraScale+ MPSoC(Multiprocessor System on Chip) device allows real-time processing of 4K resolution (UHD - Ultra High Definition,3840 × 2160 pixels) video for 60 frames per second. The system is capable of detecting a pedestrian in a single scale. The results obtained confirm the high suitability of reprogrammable devices in the real-time implementation of embedded vision systems.

   @inproceedings{badap-agh:140732,
     title = {Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream},
     projects = {Sonata},
     author = {Wasala, Mateusz and Kryjak, Tomasz},
     year = {2022},
     series = {11th~Mediterranean Conference on Embedded Computing (MECO) [Dokument elektroniczny] : 7–10 June 2022, Budva, Montenegro},
     pages = {1–6},
     doi = {10.1109/MECO55406.2022.9797113},
     collection = {11th~Mediterranean Conference on Embedded Computing (MECO) [Dokument elektroniczny] : 7–10 June 2022, Budva, Montenegro},
   }

5. Book chapter Preludium
   

   Systemy wizyjne w zastosowaniach przemyslowych

   Marek Gorgon, Maciej Aleksandrowicz, Krzysztof Blachut, Andrzej Brodzicki, Zbigniew Bublinski, and 20 more authors

   In Nauka – technika – technologia : seria wydawnicza AGH, Sep 2022

   Abstract DOI Bib

   Streszczenie: Systemy wizyjne to dynamicznie rozwijająca się dziedzina nauki i techniki.W niniejszym artykule przedstawiono przegląd zagadnień, nad którymi pracuje zespół Laboratorium Systemów Wizyjnych.Obejmuje on systemy wizyjne dla pojazdów autonomicznych, aplikacje analizy obrazów medycznych, systemy wspomagania szkolenia pracowników, analizę obrazów termowizyjnych, implementacje i akceleracje algorytmów wizyjnych w układach SoC FPGA i GPU, przetwarzanie strumienia wizyjnego w rozdzielczości 4K, a także analizę sygnałów z wykorzystaniem głębokich sieci neuronowych i współpracę przy eksperymencie ALICE w CERN-ie.

   @inbook{badap-agh:141409,
     title = {Systemy wizyjne w zastosowaniach przemyslowych},
     author = {Gorgon, Marek and Aleksandrowicz, Maciej and Blachut, Krzysztof and Brodzicki, Andrzej and Bublinski, Zbigniew and Cyba, Artur and Danilowicz, Michal and Glowacz, Adam and Jablonski, Miroslaw and Jaworek-Korjakowska, Joanna and Kostuch, Aleksander and Kowalczyk, Marcin and Kryjak, Tomasz and Kucharski, Dariusz and Lis, Konrad and Machura, Michal and Mikrut, Zbigniew and Pawlik, Piotr and Piekarski, Michal and Przewlocka-Rus, Dominika and Lis, Joanna and Szolc, Hubert and Wasala, Mateusz and Wojcicka, Anna Malgorzata and Wzorek, Piotr},
     year = {2022},
     projects = {Preludium-Kowalczyk},
     series = {Nauka – technika – technologia : seria wydawnicza AGH},
     volume = {T.~5},
     pages = {17–31},
     doi = {10.7494/978-83-67427-00-5_1},
     collection = {Nauka – technika – technologia : seria wydawnicza AGH},
   }

2021

1. DASIP

   Automotive perception system evaluation with reference data obtained by a UAV

   Krzysztof Blachut, Michal Danilowicz, Hubert Szolc, Mateusz Wasala, Tomasz Kryjak, and 2 more authors

   In DASIP 2021 : workshop on Design and Architectures for Signal and Image Processing - 14th edition : January 18–20 2021, Budapest, Hungary : proceedings, Sep 2021

   Abstract DOI Bib

   Testing and evaluation of an automotive perception system is a complicated task which requires special equipment and infrastructure. To compute key performance indicators and compare the results with the real-world situation, some additional sensors and manual data labelling are often required. In this article, we propose a different approach, which is based on a UAV equipped with a 4K camera flying above the test track. Thanks to the synchronisation of the sensors between the tested vehicle and the UAV, it is possible to precisely determine the positions of the objects around the car and correlate them with the perception system readings. The performed experiments indicate that this approach could be an interesting alternative to the existing evaluation solutions.

   @inproceedings{badap-agh:132409,
     title = {Automotive perception system evaluation with reference data obtained by a UAV},
     author = {Blachut, Krzysztof and Danilowicz, Michal and Szolc, Hubert and Wasala, Mateusz and Kryjak, Tomasz and Pankiewicz, Nikodem and Komorkiewicz, Mateusz},
     year = {2021},
     series = {DASIP~2021 [Dokument elektroniczny] : workshop on Design and Architectures for Signal and Image Processing - 14th~edition : January 18–20 2021, Budapest, Hungary : proceedings},
     pages = {10–18},
     doi = {10.1145/3441110.3441151},
     collection = {DASIP~2021 [Dokument elektroniczny] : workshop on Design and Architectures for Signal and Image Processing - 14th~edition : January 18–20 2021, Budapest, Hungary : proceedings},
   }

2020

1. ICCVG

   A vision based hardware-software real-time control system for the autonomous landing of an UAV

   Krzysztof Blachut, Hubert Szolc, Mateusz Wasala, Tomasz Kryjak, and Marek Gorgon

   In Computer Vision and Graphics : International Conference : ICCVG 2020 : Warsaw, Poland, September 14–16, 2020 : proceedings, Sep 2020

   Abstract DOI arXiv Bib

   In this paper we present a vision based hardware-software control system enabling autonomous landing of a mul-tirotor unmanned aerial vehicle (UAV). It allows the detection of a marked landing pad in real-time for a 1280 x 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption.

   @inproceedings{badap-agh:130006,
     title = {A vision based hardware-software real-time control system for the autonomous landing of an UAV},
     author = {Blachut, Krzysztof and Szolc, Hubert and Wasala, Mateusz and Kryjak, Tomasz and Gorgon, Marek},
     year = {2020},
     series = {Computer Vision and Graphics : International Conference : ICCVG~2020 : Warsaw, Poland, September 14–16, 2020 : proceedings},
     pages = {13–24},
     doi = {10.1007/978-3-030-59006-2_2},
     collection = {Computer Vision and Graphics : International Conference : ICCVG~2020 : Warsaw, Poland, September 14–16, 2020 : proceedings},
   }

2. ICCVG

   Optimisation of a Siamese neural network for real-time energy efficient object tracking

   Dominika Przewlocka-Rus, Mateusz Wasala, Hubert Szolc, Krzysztof Blachut, and Tomasz Kryjak

   In Computer Vision and Graphics : International Conference : ICCVG 2020 : Warsaw, Poland, September 14–16, 2020 : proceedings, Sep 2020

   Abstract DOI arXiv Bib

   In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting.

   @inproceedings{badap-agh:130002,
     title = {Optimisation of a Siamese neural network for real-time energy efficient object tracking},
     author = {Przewlocka-Rus, Dominika and Wasala, Mateusz and Szolc, Hubert and Blachut, Krzysztof and Kryjak, Tomasz},
     year = {2020},
     series = {Computer Vision and Graphics : International Conference : ICCVG~2020 : Warsaw, Poland, September 14–16, 2020 : proceedings},
     pages = {151–163},
     doi = {10.1007/978-3-030-59006-2_14},
     collection = {Computer Vision and Graphics : International Conference : ICCVG~2020 : Warsaw, Poland, September 14–16, 2020 : proceedings},
   }