National Repository of Grey Literature 24 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Scalable Person Identification System For Real-Time Applications
Rajnoha, Martin
Face recognition systems can play significant role in our every day lives. This paper proposes a scalable system for person identification based on face recognition methods and its implementation that utilizes queues, containers and microservices architecture. The proposed system uses a GPU acceleration therefore it can run in real-time. It utilizes two deep neural networks – Single Shot Multibox Detector (SSD) for a face detection and Facenet for a face recognition.
Face superresolution from image sequence
Mezina, Anzhelika ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
Táto práce se zabývá použitím hlubokého učení neuronových sítí ke zvýšení rozlišení obrázků, které obsahují obličeje. Tato metoda najde uplatnění v různých oblastech, zejména v bezpečnosti, například, při bezpečnostním incidentu, kdy policie potřebuje identifikovat podezřelého z nahraného videa ze sledovací kamery. Cílem této práce je navrhnout minimálně dvě architektury neuronových sítí, které budou pracovat se sekvencí snímků, a porovnat je s metodami zpracování jediného snímku. Pro tento účel je také vytvořena nová trénovací množina, obsahující sekvenci snímku obličeje. Metody zpracování jednoho snímku jsou natrénované na nové množině. Dále jsou navrženy nové metody zvětšení obrázků na základě sekvence snímků. Tyto metody jsou založené na U-Net modelu, který je úspěšný v segmentaci, ale také v superrozlišení. Pro zlepšení architektury byly použity reziduální bloky a jejich modifikace, a navíc také percepční ztrátová funkce, která dovoluje vyhnout se rozmazání a získání více detailů. První čast této práce je věnovana popisu neuronových sítí a některých architektur, jejichž modifikace mohou být použity v superrozlišení. Druhá část se poté zabývá popisem metod pro zvýšení rozlišení obrazu pomocí jednoho snímku, několika snímků a videa. Ve třetí části jsou popsány navržené metody a experimenty a v poslední části porovnaná metod založených na jednom snímku a několika snímcích. Navržené metody jsou schopny získat více detailů v obraze, ale mohou produkovat artefakty. Ty lze ale poté eliminovat pomocí filtru, například Gaussova. Nové metody méně selhávají při detekci obličejů, a to je podstatné u identifikace člověka v případě incidentu.
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.

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