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Enhancing Reliability and Benchmarking Performance of Agar Plate Handling Algorithms for Laboratory Automation Robots
Kalivodová, Tereza ; Nohel, Michal (referee) ; Mézl, Martin (advisor)
Tato bakalářská práce zkoumá problematiku vzorkové přípravy v oblasti mikrobiologie a lékařské diagnostiky s důrazem na automatizovaný robotický systém MBT Pathfinder, vyvinutý firmou \bruker. S využitím digitálních obrazových technik a konvolučních neuronových sítí se práce zaměřuje na zdokonalení algoritmu pro identifikaci pozice mikrobiálních kolonií v systému MBT Pathfinder. Praktická část práce prezentuje inovativní přístupy k optimalizaci kritických kroků vzorkové přípravy s cílem eliminovat chyby a zvýšit efektivitu procesu. Výsledky této práce mohou posílit spolehlivost mikrobiologických analýz v oblasti lékařské diagnostiky a mikrobiologického výzkumu.
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Comparison of Methods for Image Inpainting based on Deep Learning
Rajsigl, Tomáš ; Herout, Adam (referee) ; Španěl, Michal (advisor)
This bachelor thesis aims to compare deep learning methods and approaches for image inpainting using quantitative metrics like PSNR, SSIM, and LPIPS. Moreover, a user study has also been carried out for further subjective assessment. For the purposes of this comparison, four GAN-based neural networks were used. The first network, AOT-GAN, represents a benchmark against which the proposed architecture and its modifications were compared. In the experiments, a variant of the proposed method achieved a 29% improvement against AOT-GAN in images with small missing regions. This claim is also supported by the results of the user study where this method was ranked as the best. As a result of this thesis, a small dataset specifically for the evaluation of image inpainting in the context of object removal was created. Real-world applications of these methods are demonstrated through a web application.
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Design and implementation of an obstacle avoidance method in an outdoor environment for a mobile robot
Fargač, Tomáš ; Králík, Jan (referee) ; Věchet, Stanislav (advisor)
This thesis focuses on investigating the usability of the optical flow method in image processing. Firstly, this method is introduced theoretically, followed by its mathematical derivation. Subsequently, the idea of implementing it into decision-making algorithms and potential areas of application is presented. The thesis also elaborates on suitable environments for such applications in both virtual and real worlds. The practical part demonstrates the step-by-step development process and the refinement of working with this method and its outputs. The work utilizes the Matlab programming environment and detailed work at the level of individual components in this programming language, enriched with auxiliary toolboxes, especially from the field of computer vision. The entire research is summarized clearly at the end, and all undertaken steps are depicted in a flowchart. Finally, all explored approaches with their strengths and weaknesses, identified throughout the process, are clearly presented.
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Face Anti-Spoofing with Out-of-distribution Detection
Češka, Petr ; Vaško, Marek (referee) ; Špaňhel, Jakub (advisor)
This thesis aims to improve the accuracy of Vision Transformer-based face anti-spoofing models in detecting presentation attacks. The thesis uses out-of-distribution detection to filter out images that are too different from the training data, referred to as in-distribution. It examines how successful different methods are in identifying different data distributions, and how the filtering of out-of-distribution data based on these methods affects the accuracy of the model. Using the relative Mahalanobis distance, an AUROC of 97.6% can be achieved when distinguishing between in-distribution and out-of-distribution data. Filtering out images that should not be classified increases the accuracy of all tested models to over 99.9%. This can provide an additional layer of security for applications against face spoofing attacks.
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Vision Transformers for Facial Recognition
Strýček, Šimon ; Kišš, Martin (referee) ; Špaňhel, Jakub (advisor)
This thesis focuses on applying vision transformer-based neural networks to face recognition related tasks. It focuses on exploring modern vision transformer (ViT) architectures, experimenting with alternative data, and finding the suitable parameters to train ViTs to compete with the already established dominance of convolutional neural networks in face recognition. The goal of this work was to show the suitability of vision-transformers for face recognition. The output of this work contains results of various experiments, demonstrations of benefits and drawbacks of some of the modern and popular ViTs, the definition of an optimal setup when wanting to employ vision transformers for facial recognition, and interesting observations from working with vision transformers.
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Decreased visibility and image defect detection for vehicle mounted camera
Sedláček, Miloš ; Řičánek, Dominik (referee) ; Svědiroh, Stanislav (advisor)
This bachelor thesis deals with the topic of decreased visibility and image defects detection caused by adverse weather conditions or lighting from a vehicle mounted camera. The thesis describes the basic characteristics of the most common influences and their effects on camera data and presents some existing methods of detecting these influences. Next, a dataset containing selected defects is created and described. Afterwards, the issue of artificial neural networks is described in the thesis. A convolutional neural network is implemented for defect detection, which is trained and tested using the dataset. At the end, the achieved results of the network, its computational complexity and comparison with the results of other works are presented.
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Lane detection for autonomous vehicles
Holík, Štěpán ; Píštěk, Václav (referee) ; Kučera, Pavel (advisor)
This thesis focuses on the design and experimental verification of a system for lane detection, trajectory estimation and vehicle position. The goal was to develop a system composed of algorithms with its respective functions. Data collected with ZED 2 camera, the U-Net neural network model, and computer vision were used to reduce false positive predictions using a temporal window. Trigonometric calculations and camera parameters were used to estimate the vehicle’s position relative to the trajectory. One of the outcomes of this thesis is TuSimple dataset extension with the data captured with ZED 2 camera. Experimental verification demonstrated the system's functionality with high detection reliability in simple model situations, such as driving on a straight road segment. As the complexity of the model situations increased, the system's reliability decreases. Despite these shortcomings, the experiments showed that the system is able to detect lane boundaries and estimate an optimal vehicle trajectory. The algorithms for trajectory and vehicle position determination depend on the initial prediction of the lane boundaries, but they are functional and effective.
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Registration of the landing aircraft based on image recognition techniques
Juroška, Jan ; Šplíchal, Miroslav (referee) ; Červenka, Miroslav (advisor)
In this diploma thesis, a method automating for the process of registering an aircraft on an uncontrolled airfield using computer vision was created. First, aircraft is detected using neural network form the YOLO family, next, its registration is read using the Tesseract network. In the theoretical part of this work, various methods for solving this issue are introduced, as well as the theoretical basis behind the chosen method. In the practical part of this work, the documentation of the created program is provided. Testing of programs limits is conducted, and instructions for setting up and using the method are provided.
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Compensation of distortions caused by movements of objects scanned by a line scan camera
Szabó, Michal ; Shehadeh, Mhd Ali (referee) ; Škrabánek, Pavel (advisor)
Tato diplomová práce se zaměřuje na kompenzaci zkreslení v datech ještěrek získaných pomocí řádkové skenovací kamery (LSC), která snímá ultrafialové (UV) vlnové délky. Dýchací pohyby během skenování způsobují zkreslení šířky trupu ještěrky, což ovlivňuje konečný snímek LSC. Práce navrhuje metody zpracování obrazu pro úpravu těchto zkreslení, včetně extrakce kontur, interpolace a vyhodnocení pomocí referenčního snímku. Metodika má za cíl minimalizovat rozdíl mezi upraveným LSC snímkem a referenčním snímkem.
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Image registration using optimization with automatic differentiation
Slavíček, David ; Kolář, Radim (referee) ; Vičar, Tomáš (advisor)
This thesis deals with image registration using optimization with automatic differentiation. In the teoretical part were decribed steps of image registration and possibilities of their realization. The practical part consists of design of algorithm for registration of afinely transformed images and implementation of it. Two modifications of the algorith were proposed. Database of xray images was designed. The algorithm was tested on this database.
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