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Pedestrian Attribute Analysis
Studená, Zuzana ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.
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Analysis of Classification Methods
Juríček, Jakub ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This work deals with the classification methods used in the knowledge discovery from data process and discusses the possibilities of their validation and comparison. Through experiments, the work focuses on the analysis of four selected methods: Naive Bayes classificator, decision tree, neural network and SVM. Factors influencing basic characteristics such as training speed, classification speed, accuracy are examined. A part of the thesis is a desktop application, which is a tool for training, testing and validation of individual methods. Eleven reference data sets are selected for experimental purposes. At the end of this work experimental results of comparison and observed characteristics of classification methods are summarized.
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Blood vessel segmentation in retinal images using deep learning approaches
Serečunová, Stanislava ; Vičar, Tomáš (referee) ; Kolář, Radim (advisor)
This diploma thesis deals with the application of deep neural networks with focus on image segmentation. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for segmentation of objects from the image. Practical part of the work was devoted to testing of an existing network architectures. For this purpose, an open-source software library Tensorflow, implemented in Python programming language, was used. A frequent problem incorporating the use of convolutional neural networks is the requirement on large amount of input data. In order to overcome this obstacle a new data set, consisting of a combination of five freely available databases was created. The selected U-net network architecture was tested by first modification of the newly created data set. Based on the test results, the chosen network architecture has been modified. By these means a new network has been created achieving better performance in comparison to the original network. The modified architecture is then trained on a newly created data set, that contains images of different types taken with various fundus cameras. As a result, the trained network is more robust and allows segmentation of retina blood vessels from images with different parameters. The modified architecture was tested on the STARE, CHASE, and HRF databases. Results were compared with published segmentation methods from literature, which are based on convolutional neural networks, as well as classical segmentation methods. The created network shows a high success rate of retina blood vessels segmentation comparable to state-of-the-art methods.
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Detection of Traffic Signs in Image and Video
Kočica, Filip ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This thesis deals with the traffic sign detection problematics using modern techniques in image processing. Special architecture of deep convolutional neural network YOLO, i.e. You Only Look Once, which performs both detection and classification in one step, has been used. This architecture allows object detector to work on very high speeds. This thesis also deals with comparison of models trained on real and synthetic datasets. The best model trained on real dataset has reached 63.4% mAP success rate and 82.3% mAP when trained on synthetic dataset. Evaluation of one image takes about ~40.4ms on average graphics processing unit and ~3.9ms on higher than average graphics processing unit. The benefit of this thesis is that under certain conditions neural network model trained on synthetic data can achieve same or even better results than model trained on real data. This may simplify process of object detector development since it is not necessary to annotate large number of images.
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Advanced image analysis using deep neural networks
Hynek, Vojtěch ; Přinosil, Jiří (referee) ; Kiac, Martin (advisor)
This bachelor thesis deals with the problem of object detection in images using a convolutional neural network. The result of this work is a custom dataset, a neural network model YOLOv4 and a script used to process the resulting model data. The dataset contains 8080 images on which 14 objects are annotated. The neural network model was reduced in depth, which significantly increased the speed of the detection itself. The script processing the resulting data calculates the 3D and GPS coordinates of the detected object in space. The paper concludes by summarizing the results of the model and at the same time suggesting how the quality of the dataset could be improved.
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Simple Recommender System
Gorčák, Damián ; Rychlý, Marek (referee) ; Bartík, Vladimír (advisor)
Recommender systems are very important in searching for items all over the internet. There are many algorithms for creating recommendations. The main goal of this thesis was to find suitable datasets and make application, which would process them. After that, chosen algorithms for recommender systems are compared with selected datasets
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Polygonal Mesh Segmentation
Bezděčík, Ladislav ; Polášek, Tomáš (referee) ; Španěl, Michal (advisor)
This bachelor's thesis deals with the issues of segmentating 3D models of human jaws. It analyzes currently used methods and proposes, implements and tests possible improvement to these methods from user perspective. The proposal consists of using neural networks for topology recognition on jaw models, and possibly combining this topology with currently used segmentation methods. This thesis also analyzes and implements the possibility of automated expnansion of 3D model datasets converted to depth maps, used for neural network training.
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Identification and characterization of malicious behavior in behavioral graphs
Varga, Adam ; Burget, Radim (referee) ; Hajný, Jan (advisor)
Za posledné roky je zaznamenaný nárast prác zahrňujúcich komplexnú detekciu malvéru. Pre potreby zachytenia správania je často vhodné pouziť formát grafov. To je prípad antivírusového programu Avast, ktorého behaviorálny štít deteguje škodlivé správanie a ukladá ich vo forme grafov. Keďže sa jedná o proprietárne riešenie a Avast antivirus pracuje s vlastnou sadou charakterizovaného správania bolo nutné navrhnúť vlastnú metódu detekcie, ktorá bude postavená nad týmito grafmi správania. Táto práca analyzuje grafy správania škodlivého softvéru zachytené behavioralnym štítom antivírusového programu Avast pre proces hlbšej detekcie škodlivého softvéru. Detekcia škodlivého správania sa začína analýzou a abstrakciou vzorcov z grafu správania. Izolované vzory môžu efektívnejšie identifikovať dynamicky sa meniaci malware. Grafy správania sú uložené v databáze grafov Neo4j a každý deň sú zachytené tisíce z nich. Cieľom tejto práce bolo navrhnúť algoritmus na identifikáciu správania škodlivého softvéru s dôrazom na rýchlosť skenovania a jasnosť identifikovaných vzorcov správania. Identifikácia škodlivého správania spočíva v nájdení najdôležitejších vlastností natrénovaných klasifikátorov a následnej extrakcie podgrafu pozostávajúceho iba z týchto dôležitých vlastností uzlov a vzťahov medzi nimi. Následne je navrhnuté pravidlo pre hodnotenie extrahovaného podgrafu. Diplomová práca prebehla v spolupráci so spoločnosťou Avast Software s.r.o.
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Exploitation of Machine Learning for Identification of Feeder Rod Movement
Vele, Patrik ; Vašíček, Zdeněk (referee) ; Šimek, Václav (advisor)
The aim of this diploma thesis is to create a device that uses machine learning methods to recognize the movements of a feeder fishing rod based on data from an inertial measurement unit. The introductory part is devoted to the feeder fishing technique, the selection of important movements and the possibilities of attaching the detection device to the rod. This is followed by the creation of a theoretical basis in the field of machine learning, familiarization with the inertial measurement unit and the issue of classification. The acquired knowledge is used to select appropriate techniques for solving the task of recognizing the movements of the rod. In the practical part, a detection device based on the ESP32 platform is designed and created. This is initially used as a motion sensor, which, in combination with the processing of the measured values, serves as a generator of a training data set. The work continues with the implementation of the convolutional neural network, the learning process on the created dataset and the integration of the most successful model into the detection device. The conclusion is devoted to testing in practice, evaluation and possibilities of future development. The result is a small, battery-powered device that, when attached to any feeder rod, provides highly successful detection of all key movements during the hunt. In addition, thanks to wireless communication via ESP-NOW, it is possible to send the results to various devices.
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Design of Methods for Encrypted Traffic Visualization
Hlučková, Pavla ; Martinásek, Zdeněk (referee) ; Malina, Lukáš (advisor)
This thesis deals with design of methods for encrypted traffic visualization. It generally describes selected encrypted traffic protocols, whose data samples were collected later on to form a dataset. Furthermore, it focuses on the topic of IP flow monitoring and decribes the means of carrying out such monitoring. An important part of this thesis is the dataset created from the samples of mentioned protocols and the visualizations of different statistics and metadata gatherable from (extended) IP flows of these protocols. The designed methods of visualization are implemented using the Python programming language and the Jupyter Notebook technology.
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