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Neural Network Based Face Localization
Hendrych, Pavel ; Šiler, Ondřej (referee) ; Švub, Miroslav (advisor)
This thesis issues with possible methods for face detection and localization according to the state of the art. It describes various approaches and it is aimed at localization by neural networks and at necessary operations that have to be done before localization and after that for correct results representation. This project contains implementation of few approaches to neural netwok based face localization with emphasis on eigenfaces based face localization as well as implementation of simple classifier using distance of reconstructed face to the original one. Detailed description of implemented system, achieved results and dependecy of system performance on it's inner settings is also provided.
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Object Detection in Images
Vaľko, Tomáš ; Motlíček, Petr (referee) ; Švub, Miroslav (advisor)
Object detection in images is quite popular topic for years. What stands for it are a lot of works from this area of computer science. This thesis is about object classification, specifically human faces, which are one of the most interesting objects for processing. For classification we use neural networks, learned on face database. We study what influence has size of face database and preprocessing of digital image on neural network learning. This project implements simple face detector and localizator. It summarizes more and less successful results and indicates possible ways of system development in the future.
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Handwritten Character Recognition Using Artificial Neural Networks
Smejkal, Vojtěch ; Fapšo, Michal (referee) ; Plchot, Oldřich (advisor)
Thesis deals with handwritten block letters and digits recognition using artificial neural networks. Text segmentation algorithms, feature extraction methods and backpropagation learning are explained. There are also described performed experiments with variety of configurations on datasets. Application with graphical user interface and interactive mouse-written text recognition was created to train new neural networks and test their effectivity.
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Handwritten Character Recognition Using Artificial Neural Networks
Smejkal, Vojtěch ; Fapšo, Michal (referee) ; Plchot, Oldřich (advisor)
Thesis deals with handwritten block letters and digits recognition using artificial neural networks. Text segmentation algorithms, feature extraction methods and backpropagation learning are explained. There are also described performed experiments with variety of configurations on datasets. Application with graphical user interface and interactive mouse-written text recognition was created to train new neural networks and test their effectivity.
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Object Detection in Images
Vaľko, Tomáš ; Motlíček, Petr (referee) ; Švub, Miroslav (advisor)
Object detection in images is quite popular topic for years. What stands for it are a lot of works from this area of computer science. This thesis is about object classification, specifically human faces, which are one of the most interesting objects for processing. For classification we use neural networks, learned on face database. We study what influence has size of face database and preprocessing of digital image on neural network learning. This project implements simple face detector and localizator. It summarizes more and less successful results and indicates possible ways of system development in the future.
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Neural Network Based Face Localization
Hendrych, Pavel ; Šiler, Ondřej (referee) ; Švub, Miroslav (advisor)
This thesis issues with possible methods for face detection and localization according to the state of the art. It describes various approaches and it is aimed at localization by neural networks and at necessary operations that have to be done before localization and after that for correct results representation. This project contains implementation of few approaches to neural netwok based face localization with emphasis on eigenfaces based face localization as well as implementation of simple classifier using distance of reconstructed face to the original one. Detailed description of implemented system, achieved results and dependecy of system performance on it's inner settings is also provided.
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