National Repository of Grey Literature 77 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
User Interface for HDR Tone Mapping System
Jedlička, Jan ; Špaňhel, Jakub (referee) ; Čadík, Martin (advisor)
The goal of this thesis is to improve graphical user interface of Tone Mapping Studio(TMS) program. This program is being developed on the Faculty of Information Technology(FIT), Brno University of Technology (BUT) by doc. Ing. Martin Čadík, PhD. The current program is using framework Qt3 , which is old and not compatible with modern libraries. This program has to be rewritten to support current version Qt5. I will analyze other programs in the area of working with High Dynamic Range (HDR) images and video. Changes for improving the interface will be proposed and UX tests will be done. Second part will consist of comparing plug-ins for converting images to grayscale that already exists in TMS.
Data Interface for Sharing of "City Data"
Fiala, Jan ; Špaňhel, Jakub (referee) ; Zemčík, Pavel (advisor)
The goal of this thesis is to explore existing solutions of closed and open data sharing, propose options of sharing non-public data, implement selected solution and demonstrate the functionality of the system for sharing closed data. Implementation output consist of a catalog of non-public datasets, web application for administration of non-public datasets, application interface gateway and demonstration application.
Vehicle Counting in Still Image
Hladiš, Martin ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The goal of this thesis is to compare different models of convolutional neural networks, which use the principle of using density estimation to count the number of vehicles in a still image. The tested models were -- Counting CNN, Scale-adaptive CNN, Multi-Scale Fusion Net a Multi-scale CNN. Their estimation capability was tested using these datasets -- TRANCOS, CARPK, PUCPR+. The most accurate results were achieved by the Multi-Scale Fusion Net model. Its estimation accuracy using the dataset TRANCOS in the Mean Absolute Error metric achieved value of 8.05.
Innovative Music Player for Smartphones and PC
Richter, Roman ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
The goal of this thesis is to create a music player for smartphones as well as PCs that works with local music files in the user's device and which can learn which songs does the user like based on their actions during listening to music. The music player can, among other things, remember which songs were skipped by the user, when was volume turned up, or how many times was a certain song played. Each song has a score that is calculated based on these actions. With a higher score, there is also a higher chance of playing the song in the future. The results of my thesis are two full-featured versions of music player, which are capable of communication with each other to ensure synchronization of song scores. The main benefit of this thesis is an improvement of user experience during listening to music, which is achieved by the application's algorithm for song selection and minimalistic user interface.
Unique Car Counting
Uhrín, Peter ; Špaňhel, Jakub (referee) ; Juránek, Roman (advisor)
Current systems for counting cars on parking lots usually use specialized equipment, such as barriers at the parking lot entrance. Usage of such equipment is not suitable for free or residential parking areas. However, even in these car parks, it can help keep track of their occupancy and other data. The system designed in this thesis uses the YOLOv4 model for visual detection of cars in photos. It then calculates an embedding vector for each vehicle, which is used to describe cars and compare whether the car has changed over time at the same parking spot. This information is stored in the database and used to calculate various statistical values like total cars count, average occupancy, or average stay time. These values can be retrieved using REST API or be viewed in the web application.
Vehicle Detection in Image and Video
Rozprým, Dalimil ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The goal of this thesis is comparison of available multiclass detectors abilities to detect road vehicles on purposely created dataset. As multiclass detectors are chosen neural networks for detection and classification of objects in image. Detectors described in this text and used for experimentation are Mask R-CNN, YOLOv4 and YOLACT++. This selection encompasses multiple different architectures and approaches to object detection. Created dataset used for learning and testing is thoroughly described in this text. Detection capability of detectors is tested on images from casual traffic and separately on partially covered objects. The outcome of this thesis is reusable and expandable dataset, measured performance values and their deeper exploration in this text. 
Easily Configurable Visual Inspection System for Industrial Applications
Andrla, Ondřej ; Špaňhel, Jakub (referee) ; Španěl, Michal (advisor)
The aim of this thesis is to create a prototype of an easily configurable camera system, used to control products on an automated assembly line. The thesis is especially focused on the issue of LED detection. Their very presence in the image is detected and the system also evaluates whether the LED is lit and, if so, in what color. The final solution allows the user to easily define control metric for searching for circular objects in the image in a configuration application with a graphical user interface. The configuration is saved in a JSON file. Furthermore, a runtime which independently processes the recording from the camera, checks the products and reports on them to other components of the system based on the configuration was implemented. The system uses the Hough circular transformation to detect objects in the image. To test the efficiency of the system, a dataset was created using the FitKit tool with a LED matrix. When testing the runtime on the dataset, the overall accuracy of LED detection was 97,79%.
Counting Crates in Images
Mičulek, Petr ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
V této práci se zabývám tématem počítání beden v obrazových datech pomocí technik hlubokého učení. V práci jsem navrhl řešení pro počítání beden, které představuji na fotkách krabiček sirek. Ačkoli původní řešení počítalo s využitím datové sady beden ze skladu pivovaru, sada nakonec nebyla dodána a na doporučení vedoucího práce byly pro řešení vybrány bloky krabiček sirek. Implementované řešení využívá plně konvoluční neuronovou síť založenou na klasifikaci, umožňující výstup ve vysokém rozlišení. Tato síť je trénována na výřezech fotek z datové sady, díky čemuž je řešení rychlé a síť je vhodná i pro použití na menších datových sadách. Síť detekuje ve fotkách klíčové body krabiček sirek, které jsou následně zpracovány algoritmem pro odhad klíčových bodů z predikce sítě a výpočet finálního počtu beden. Na validačním datasetu dosahuje řešení následujících výsledků: ve 12,5 % případů predikce selže a ve zbylých případech má průměrnou absolutní odchylku (MAE) 11,14. Pomocí rozsáhlých experimentů bylo řešení vyhodnoceno a výsledky potvrzují, že tento přístup může být použit pro počítání objektů.
Occupancy Estimation of a Parking Lot from Images
Dubovec, Pavol ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
When determining the number of vehicles in the pictures of carparks that do not have the appropriate parameters needed for processing, the problem of vehicle counting can be quite complex. The aim of this work is to create an application that detects the number of vehicles in the selected photo, regardless of selected carpark view. This detection will be performed using machine learning, based on a model, created by training on trained data, which consists of photographs of parking lots from different perspectives and positions. The problem was solved in an unconventional way, by splitting the pictures with the parking lot into several areas of interest (zones) and creating anotations from these areas, using the created application specialized for this task. The images are then formatted to the same size. These prepared cutouts are then loaded to the Keras API, which is used to train the model. The aim was to create a model that would be versatile enough to determine the number of vehicles in a photograph in any environment (time, weather, weather conditions) and in the shortest possible time. Currently, the model can predict the correct number of vehicles in the cutout on test data with an accuracy of 87% and with a first order error of 95%. This work focuses on solving this problem in real time. It is a classification into 7 classes (0-6 vehicles). This solution could be interesting especially for static cameras in atypical places (eg side view), or it is important for them to capture certain areas.
Detection of Boxes in Image
Žitňanský, Adam ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
This thesis addresses the problem of cuboid detection, more specifically boxes detection in images. The main result is the implementation of a system for boxes detection based on corners and edges. The system consists of a CNN regression-based corner and edge points detector and decoder, which takes CNN output and turns it into a 2d model of the cuboid. As a part of this work also a a dataset of boxes with 550 images with corners and edges annotations was created

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1 Špaňhel, Jan
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