National Repository of Grey Literature 74 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Tracking of moving object in video
Komloši, Michal ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This master thesis deals with tracking the moving object in image. The result of the thesis is designed algorithm which is implemented in the programming language C#. This algorithm improves the functionallity of an existing tracking algorithm.
Tracking of moving object in video
Komloši, Michal ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This master thesis deals with tracking the moving object in image. The result of the thesis is designed algorithm which is implemented in the programming language C#. This algorithm improves the functionallity of an existing tracking algorithm.
Deep learning based sound event recognition
Bajzík, Jakub ; Kiska, Tomáš (referee) ; Přinosil, Jiří (advisor)
This paper deals with processing and recognition of events in audio signal. The work explores the possibility of using audio signal visualization and subsequent use of convolutional neural networks as a classifier for recognition in real use. Recognized audio events are gunshots placed in a sound background such as street noise, human voice, animal sounds, and other forms of random noise. Before the implementation, a large database with various parameters, especially reverberation and time positioning within the processed section, is created. In this work are used freely available platforms Keras and TensorFlow for work with neural networks.
Software for manual focus of camera with 4K resolution
Sláma, Adam ; Přinosil, Jiří (referee) ; Kříž, Petr (advisor)
This Master thesis is focused on the analysis of currently used methods which whose target is to determine the rate of image focus. This analysis was used during the development of the program which evaluates the rate of image focus in percentage rate, works in real time and cooperates with a camera capable of 4k image resolution with a manual focus of the lenses. Application is then capable of a finding of a pre-defined image under certain circumstances which is being used for increasing of effectivity of image focusing. Another option is represented by a method that is searching the most suitable area for focusing in the center of the image. A detailed description of these methods and program itself are also included in the thesis. The final part of the thesis contains records of measurement tests with its results.
Food classification using deep neural networks
Kuvik, Michal ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. Subsequently, all results are compared and discussed with the best result achieved an accuracy of 86, 44% on test data.
Deep learning based face recognition in real conditions
Horňáková, Veronika ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
This bachelor thesis explores the area of face recognition using deep learning technique. Face recognition is used for two main reasons: verification and identification. In this thesis we describe the techniques of deep learning, mostly the convolutional neural networks, which are the most significant method for processing images - detection, classification and segmentation of the image. The process of face recognition is divided into four main steps: face detection, face selection, face extraction and face classification. We chosen three of the existing programs for face recognition (OpenFace, FaceNet and Face_Recognition), which are described in this thesis, in particular the principle of the human face recognition. Thanks to the tests with the data set of Labeled Faces in the Wild (LFW) we could specify the accuracy and the time requirement of each application. Testing of FaceNet and Face_Recognition ran on real data with face detection in video with complicated conditions. The test compares two images and tries to determine if is the same person. The test results are show in graph and table.
Object tracking in video
Boszorád, Matej ; Přinosil, Jiří (referee) ; Rajnoha, Martin (advisor)
This bachelor thesis deals with the issue of tracking multiple objects in a video, specifically focusing on non-learning algorithms. The first chapter represents the theoretical part of the thesis, in which some of the often used tracking methods are described, such as mean-shift, scale-invariant object transformation, Kalman filter, particle filter and Gabor wavelet transformation. These algorithms are broken down by properties they use for proper tracking. The chapter also contains section assignment problem, which is mainly concerned with Hungarian algorithm. The next part describes options of merging multiple tracking methods that are broken down by construction type into parallel, cascade, weighted and discriminatory with example for each one. Moreover there is described adaptability of the tracking system. Bellow are described problems which may occur during tracking and possible solutions to them. This section consists of a solution of image noise, changes in illumination, appearance and extinction of an object, focusing mainly on solving the problem of object occlusion. Within the practical part is created algorithm composed of different types of tracking, the results of which are then compared with selected tracking systems from the multiple object tracking benchmark. The practical part includes the tools used and the explanation of the design, in which the main classes and methods used for the tracking are explained. Besides that, this section describes parallel merging and tracking adaptability . The results of the thesis contain a comparison of the use of tracking techniques separately and together. To compare the results, videos for pedestrian tracking and face tracking were used. This thesis was based on the assumption that merging multiple monitoring systems will help with the improvement of the tracking, which was confirmed by the results.
Software for Digital Mixing Console
Zoň, Robin ; Přinosil, Jiří (referee) ; Schimmel, Jiří (advisor)
This thesis describes the design and implementation of a software for digital mixing console built on the Windows platform. This software is designed to offer real-time multi-channel audio processing using multiple input and output units, signal routing between these units and insertion and management of VST plug-in modules. The software uses an audio interface connected with ASIO technology. The thesis is divided into several applications. Main application which computes audio samples and allows insertion and management of plug-ins is programmed in C++ using JUCE technology. This application can be controlled with its own local graphical interface or with web control interface, which is programmed in TypeScript with the use of React technology. Web interface allows user to control VST plug-in modules with its own custom implementation of plug-in control.
Superresulution of photography using deep neural network
Holub, Jiří ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
This diploma thesis deals with image super-resolution with conservation of good quality. Firstly, there are described state of the art methods dealing with this problem, as well as principles of neural networks with focus on convolutional ones. Finally, there is described a few models of convolutional neural network for image super-resolution to double size, which have been trained, tested and compared on newly created database with pictures of people.

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