National Repository of Grey Literature 22 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
The effect of the background and dataset size on training of neural networks for image classification
Mikulec, Vojtěch ; Kolařík, Martin (referee) ; Rajnoha, Martin (advisor)
This bachelor thesis deals with the impact of background and database size on training of neural networks for image classification. The work describes techniques of image processing using convolutional neural networks and the influence of background (noise) and database size on training. The work proposes methods which can be used to achieve faster and more accurate training process of convolutional neural networks. A binary classification of Labeled Faces in the Wild dataset is selected where the background is modified with color change or cropping for each experiment. The size of dataset is crucial for training convolutional neural networks, there are experiments with the size of training set in this work, which simulate a real problem with the lack of data when training convolutional neural networks for image classification.
People recognition using facial images
Lindovský, Michal ; Burget, Radim (referee) ; Rajnoha, Martin (advisor)
This bachelor thesis focuses on the person recognition between several millions of people in a few seconds. As a part of my thesis is comparison of two programs which are used for recognizing faces - OpenFace and Face Recognition. Computing times of localization and face encoding are compared. The accuracy of recognition in various tests is compared as well, such as blurred image, brightness changes, age of person or usage of sunglasses. Created web application is made for recognizing people in different databases. Is possible to add or remove databases of people in the application. The application allows to subsume people into database by gender automatically or manually. Face recognition can be speeded up by using multiple processor cores.
Face parameterization using videosequence
Lieskovský, Pavol ; Mekyska, Jiří (referee) ; Rajnoha, Martin (advisor)
This work deals with the problem of face parameterization from the video of a speaking person and estimating Parkinson’s disease and the progress of its symptoms based on face parameters. It describes the syntax and function of the program that was created within this work and solves the problem of face parameterization. The program formats the processed data into a time series of parameters in JSON format. From these data, a dataset was created, based on which artificial intelligence models were trained to predict Parkinson’s disease and the progress of its symptoms. The process of model training and their results are documented within this work.
Dataset generation for specific cases of face recognition
Kolmačka, Tomáš ; Kolařík, Martin (referee) ; Rajnoha, Martin (advisor)
The diploma thesis deals with current problems of person identification and deep learning. Furthermore, the work deals mainly with obtaining quality and diverse data that are used to train deep learning with convolutional neural networks for face recognition. There is very little public access to such data, so the practical part focuses on creating the MakeHuman plugin that will generate a database of random face images. It is possible to generate faces according to five different scenarios in which purely random faces or faces where the same can be seen with modifications such as different hair, beard, hat, glasses and more are created. The scenarios also allow you to generate faces with some expressions or faces as they age. You can set some parameters that give the appearance of the resulting database in the plugin. This can include face images from different angles of rotation, zooming and lighting.
Prototype Verification of Modification of Evolutionary Algorithm
Švestka, Marek ; Rajnoha, Martin (referee) ; Šeda, Pavel (advisor)
This thesis is about evolutionary algorithms with a concrete solution for an Aircraft Landing Problem. The goal is to create a genetic algorithm for this task resolution, apply selected modifications and compare all outputs. The program runs with different selection methods which are further reviewed. Input data are taken from Operations research library for this task. The outcome of this thesis gives a closer look to evolutionary programming and it’s problem resolution.
Image segmentation of unbalanced data using artificial intelligence
Polách, Michal ; Rajnoha, Martin (referee) ; Kolařík, Martin (advisor)
This thesis focuses on problematics of segmentation of unbalanced datasets by the useof artificial inteligence. Numerous existing methods for dealing with unbalanced datasetsare examined, and some of them are then applied to real problem that consist of seg-mentation of dataset with class ratio of more than 6000:1.
Realtime Pedestrian Recognition Using Siamese Network
Rajnoha, Martin
Image similarity measuring has many various applications. Pedestrian recognition is one of them and for the security purposes it is basically required to run in real-time. This paper proposes a deep Siamese neural network architecture for pedestrian recognition that achieves 70.28% accuracy on the test set containing 20 persons. Prediction of the model is fast enough for real-time processing.
Warehouse Modeling Using Graphical User Interface
Rajnoha, Martin
This paper describes a new algorithm which enable efficient conversion of graphical representation of warehouse into graph theory representation and consequently accelerates estimation for route costs. The proposed algorithm computes route distances between any place in warehouses and does so significantly faster than traditional approaches. For this purpose an algorithm based on Breadth first search, image processing “skeletonization” and Dijkstra algorithm was proposed. Using the proposed algorithm it is possible to search routes in a warehouse effectively and fast using precomputed routing table. Searching time is approximately hundreds of microseconds using routing table and even it is independent on size of warehouse instead of using Dijkstra algorithm.
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.
Computational tasks for Parallel data processing course
Horečný, Peter ; Rajnoha, Martin (referee) ; Mašek, Jan (advisor)
The goal of this thesis was to create laboratory excercises for subject „Parallel data processing“, which will introduce options and capabilities of Apache Spark technology to the students. The excercises focus on work with basic operations and data preprocessing, work with concepts and algorithms of machine learning. By following the instructions, the students will solve real world situations problems by using algorithms for linear regression, classification, clustering and frequent patterns. This will show them the real usage and advantages of Spark. As an input data, there will be databases of czech and slovak companies with a lot of information provided, which need to be prepared, filtered and sorted for next processing in the first excercise. The students will also get known with functional programming, because the are not whole programs in excercises, but just the pieces of instructions, which are not repeated in the following excercises. They will get a comprehensive overview about possibilities of Spark by getting over all the excercices.

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