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Detection of a Yoga Poses in Image
Kutálek, Jiří ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The motivation for this thesis is the concept of a smartphone app detecting Yoga poses and displaying Yoga frames to a user. The goal of this project is proving that even a simple Convolutional Neural Network (CNN) model can be trained to recognize and classify video frames from a Yoga session. I created an application in which the videos are manually annotated. The data, consisting of frames captured from 162 collected videos based on the annotations, is then passed to train a CNN model. The Dataset consists of 22 000 images of 22 different Yoga poses. The frames are captured using the OpenCV library, the training process is handled by the TensorFlow platform and the Keras API, and the results are visualized in the TensorBoard toolkit. The Model's multi-class classification accuracy reaches 91% when the binary cross-entropy loss function and the sigmoid activation function are used. Despite the promising experimental results, the main contributions are the dataset forming tools and the Dataset itself, which both helped to confirm the proof-of-concept.
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Realistic Visualisation of Alcoholic Beverages
Bartl, Vojtěch ; Kučiš, Michal (referee) ; Polok, Lukáš (advisor)
The object of thesis was to make a simple program for visualisation of alcoholic beverages. There was used a global illumination method raytracing, which serve the purpose to visualize 3D scene in photorealistic quality. The rays sent to the scene reflects and refracts on the interfaces and getting information about color of materials. Finally the ray determine the color of pixel. It's a very complicated process where it is necessary to solve passing rays through different interfaces.
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Mobile Application with Computer Vision Elements
Dyk, Tomáš ; Bartl, Vojtěch (referee) ; Špaňhel, Jakub (advisor)
Bachelor thesis describes development of mobile applications for loading and solving sudoku. Mobile application is developed for Android. The user loads a sudoku into the application by taking a picture of sudoku from a newspaper or a magazine. The application then detects the sudoku position in the photo, recognizes the numbers in each sudoku field and allows the user to solve the sudoku in the mobile phone. All unresolved sudoku, including newly uploaded, is stored in the application so that the user can solve it at any time.
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Web App for Q-Sorting
Janů, Michal ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
Hlavním cílem této práce je vytvořit a iterativně otestovat uživatelské rozhraní (UI) s uživatelskou zkušeností (UX) pro aplikaci podporující Q-řazení, se zaměřením na třídění karet. Aplikace umožňuje řazení (přesun a výměnu) karet obsahující tvrzení, která jsou řazena na základě určité otázky. Dále jsou podporovány další funkce a elementy, které usnadňují třídění karet a celkovou práci s aplikací. Aplikace například umožňuje resetovat celou tabulku s kartami, automaticky ukládá už částečně seřazenou tabulku a poskytuje "minimap" element s náhledem nad celou tabulkou.
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Web App for Q-Sorting
Daněk, Tomáš ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The aim of my bachelor's thesis was to become familiar with the Q-methodology and create a web application for conducting research according to this method. The work includes a complete report on the technologies used and the overall implementation of the web application. I focused mainly on the user interface and ease of handling the application for the respondents. Thanks to this application, the client is able to carry out his own research and the respondents of the research are provided with user interface that is very intuitive and easy to use.
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Approximation of Sound Propagation by Neural Networks
Nguyen, Son Hai ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
Za účelem nahrazení výpočtově náročných konvenčních numerických metod řešících diferenciální rovnice jsou neurální výpočty stále více prozkoumávány. Tato práce se zaměřuje na řešení časově nezávislé Helmholtzovi rovnice, která modeluje šíření ultrazvuku při transkraniální léčbě ultrazvukem. Při použití konvolučních neuronových sítí musí být data navzorkovaná na pravidelné mřížce, abychom odstranili dané omezení, navrhli jsme neurální výpočet založený na grafových neuronových sítích. Narozdíl od fyzikálně informovaných neuronových sítích (PINN) je potřeba náš model natrénovat pouze jednou, řešení pro množinu nových parametrů vyžaduje pouze dopředných chod. Model byl natrénovaný pomocí učení s učitelem, kde referenční data byly vypočítána pomocí konvenční metody k-Wave. Náš model má stabilní rozvinutí, přestože byl natrénovaný pouze s osmi iteracemi. Ačkoli byl model natrénovaný pouze na datech s jedním zdrojem vln, tak zvládne predikovat i vlnová pole s více zdroji i v mnohem větších výpočetních doménách. Náš model je schopen predikovat subpixelové body s větší přesností než lineární interpolace. Dále je naše řešení schopno predikovat vlnové pole i s podvzorkovaným Laplaciánem, kde jsou pouhé tři vzorky na jednu vlnovou délku. Nejsme si vědomi žádné existující metody fungující s takto řídkou diskretizací.
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Smartwatch App for Sports Training and Competitions
Dohnalík, Pavel ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The aim of the work is to create an application for a smart watch, which will allow you to measure races and trainings, or create localization data for this activity. The application is implemented for mobile devices with the Android and iOS operating systems. The Wear OS operating system is supported for smart watches. The thesis describes the theory of programming for mobile operating systems and programming for the operating system Wear OS. The practical part describes the design, implementation and testing. For the implementation of the mobile application as well as for the smart watch application I decided to choose Flutter framework and programming language Dart. The resulting application allow users to measure races and workouts.
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Detection of a Yoga Poses in Image
Kutálek, Jiří ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The motivation for this thesis is the concept of a smartphone app detecting Yoga poses and displaying Yoga frames to a user. The goal of this project is proving that even a simple Convolutional Neural Network (CNN) model can be trained to recognize and classify video frames from a Yoga session. I created an application in which the videos are manually annotated. The data, consisting of frames captured from 162 collected videos based on the annotations, is then passed to train a CNN model. The Dataset consists of 22 000 images of 22 different Yoga poses. The frames are captured using the OpenCV library, the training process is handled by the TensorFlow platform and the Keras API, and the results are visualized in the TensorBoard toolkit. The Model's multi-class classification accuracy reaches 91% when the binary cross-entropy loss function and the sigmoid activation function are used. Despite the promising experimental results, the main contributions are the dataset forming tools and the Dataset itself, which both helped to confirm the proof-of-concept.
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Detection of Boxes in Image
Soroka, Matej ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The aim of this work is to experiment and evaluate different approaches of computer vision with the aim of automatic detection of boxes-blocks in the image, for this purpose, approaches based on neural networks were used in the solution. Experiments were performed with classification using our own data set, classification using our own convolutional neural network, detection using a window, YOLO detector and in the last part a proposal for improvement using U-net and MirrorNet networks.
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