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Non-contact measurement of the dimensions of determination scales
Šemora, Petr ; Matoušek, Radomil (referee) ; Škrabánek, Pavel (advisor)
This thesis deals with non-contact measuring the dimensions of the sand lizard anal plate. First the thesis briefly summarizes the techniques used to measure object dimensions and the techniques used for image segmentation. Subsequently, the thesis provides a basic overview of neural networks and convolutional neural networks. The practical part describes a system for measuring the dimensions of the sand lizard anal plate. The proposed algorithms are implemented in a graphical user interface enabling automatic and manual measurements.
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Face detection and recognition with use of Raspberry Pi
Rozhoňová, Andrea ; Mézl, Martin (referee) ; Hesko, Branislav (advisor)
The following bachelor thesis is focused on the face detection and recognition in an image. The theoretical part divides methods of detection and recognition into several groups and there is better description and explanation of these methods in this part. At the end of the theoretical part is summarized the current utilization of person recognition on the bases of its face in practice. In the practical part is first implemented method for face detection. It is combination of two approaches - approach using haar features and approach using templates of eye. The face recognition is provided by the convolutional neural network. In conclusion there are summarized principles and problems associated with implementation on microcomputer Raspberry Pi and there is also evaluated the success of implemented methods.
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Anti-Drone Perimeter Protection
Janík, Roman ; Dvořák, Michal (referee) ; Drahanský, Martin (advisor)
Developement of drone technology brings opportunities for many fields of human activity, but simultaneously brings security threats. A need to effectively face these threats arises. In this work is described the problematics and state-of-the-art methods for object detection in a video captured by moving camera. A system for detecting and locating a drone or a flock of drones has been proposed. Algorithm for detection is based on convolutional neural network, specifically on SSD algorithm. The convolutional neural network was trained on self-made dataset. The system was implemented using OpenCV library with possible algorithm acceleration on GPU using OpenCL. Created solution was tested on video.
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Pedestrians Detection in Traffic Environment by Machine Learning
Tilgner, Martin ; Klečka, Jan (referee) ; Horák, Karel (advisor)
Tato práce se zabývá detekcí chodců pomocí konvolučních neuronových sítí z pohledu autonomního vozidla. A to zejména jejich otestováním ve smyslu nalezení vhodné praxe tvorby datasetu pro machine learning modely. V práci bylo natrénováno celkem deset machine learning modelů meta architektur Faster R-CNN s ResNet 101 jako feature extraktorem a SSDLite s feature extraktorem MobileNet_v2. Tyto modely byly natrénovány na datasetech o různých velikostech. Nejlépší výsledky byly dosaženy na datasetu o velikosti 5000 snímků. Kromě těchto modelů byl vytvořen nový dataset zaměřující se na chodce v noci. Dále byla vytvořena knihovna Python funkcí pro práci s datasety a automatickou tvorbu datasetu.
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Bayesian and Neural Networks
Hložek, Bohuslav ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural networks and Bayes rule is summarized in the first part of this paper. Principles of Occams razor and Bayesian neural network are explained. A real case of use is introduced (about predicting landslide). The second part of this paper introduces how to construct Bayesian neural network in Python. Such an application is shown. Typical behaviour of Bayesian neural networks is demonstrated using example data.
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Music Recommendation System
Páleník, Radoslav ; Hrubý, Martin (referee) ; Zbořil, František (advisor)
This bachelor thesis aims to study and design computer program, which will be able to recognise 10 essential music genres using deep learning. This classification was implemented by convolutional neural network based on Tensorflow framework. This network process audio file into segments in form of spectograms and returns percentual propability of record being classified into specific genre by features found in spectogram.
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Design of the application for the camera control and machine learning
Lukaszczyk, Jakub ; Richter, Miloslav (referee) ; Bilík, Šimon (advisor)
This bachelor thesis deals with the design of a program for controlling industrial cameras. The first part deals with current applications, their design and shortcomings. In the practical part, a similar application is then developed using Python. Compared to currently available applications, the developed application provides a modular and open design and can therefore be further extended and modified. The application is further complemented with a link to the Tensorflow library to enable image classification and training of artificial neural network models. The application has been tested and appears to be functional. The thesis concludes by evaluating the results and outlining possibilities for further development.
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Supporting Board Game Nemesis on Android Mobile Phone
Štěpánek, Miroslav ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create a mobile application for the board game Nemesis designed for the Android system, which will allow the user to find out information about the game components during the game. The solution consists of two main parts the first is a model created with the help of the Tensorflow library, which is responsible for the detection of these components. The second is the application itself, which receives results from the model and displays the resulting information to the user. This makes the game easier for the user and helps to speed it up. The resulting system can be modified so that the application can be used for other games.
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Deep Learning for Medical Image Analysis
Dronzeková, Michaela ; Kodym, Oldřich (referee) ; Španěl, Michal (advisor)
The purpose of this thesis is to use convolutional neural networks for X-ray image classification of human body. Four different architectures of neural networks have been created. They were trained and tested on three tasks: classification of front and lateral chest, classification of X-ray images into several different categories and classification of diseases in chest X-ray. ResNet and SEResNet architectures achieved the best results. SEResNet scored 99,49% accuracy in the first task, ResNet achieved 94,97% accuracy in the second task and SEResNet reached 31,53% in the third task with F1 measure as metrics for evaluating results.
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