In this thesis, we investigate the problem of human layout estimation in unconstrained still images. This involves predicting the spatial configuration of body parts. We start our investigation with pictorial structure models and propose an efficient method of model fitting using skin regions. To detect the skin, we learn a colour model locally from the image by detecting the facial region. The resulting skin detections are also used for hand localisation. Our next contribution is a comprehensive dataset of 2D hand images. We collected this dataset from publicly available image sources, and annotated images with hand bounding boxes. The bounding boxes are not axis aligned, but are rather oriented with respect to the wrist. Our dataset is quite exhaustive as it includes images of different hand shapes and layout configurations. Using our dataset, we train a hand detector that is robust to background clutter and lighting variations. Our hand detector is implemented as a two-stage system. The first stage involves proposing hand hypotheses using complementary image features, which are then evaluated by the second stage classifier. This improves both precision and recall and results in a state-of-the-art hand detection method. In addition we develop a new method of non-maximum suppression based on super-pixels. We also contribute an efficient training algorithm for structured output ranking. In our algorithm, we reduce the time complexity of an expensive training component from quadratic to linear. This algorithm has a broad applicability and we use it for solving human layout estimation and taxonomic multiclass classification problems. For human layout, we use different body part detectors to propose part candidates. These candidates are then combined and scored using our ranking algorithm. By applying this bottom-up approach, we achieve accurate human layout estimation despite variations in viewpoint and layout configuration. In the multiclass classification problem, we define the misclassification error using a class taxonomy. The problem then reduces to a structured output ranking problem and we use our ranking method to optimise it. This allows inclusion of semantic knowledge about the classes and results in a more meaningful classification system. Lastly, we substantiate our ranking algorithm with theoretical proofs and derive the generalisation bounds for it. These bounds prove that the training error reduces to the lowest possible error asymptotically.

This master thesis deals with usage of machine learning methods in computer vision for classification of unknown images. The first part contains research of available machine learning methods, their limitations and also their suitability for this task. The second part describes the processes of creating training and testing gallery. In the practical part, the solution for the problem is proposed and later realised and implemented. Proper testing and evaluation of resulting system is conducted.

Master's thesis deals with multiclass image segmentation using convolutional neural networks. The theoretical part of the Master's thesis focuses on image segmentation. There are basics principles of neural networks and image segmentation with more types of approaches. In practical part the Unet architecture is choosen and is described for image segmentation more. U-net was applied for medicine dataset. There is processing procedure which is more described for image proccesing of three-dimmensional data. There are also methods for data preproccessing which were applied for image multiclass segmentation. Final part of current master's thesis evaluates results.

This thesis is focused on designing and implementing system, that adds learning and planning capabilities to agents designed for playing real-time strategy games like StarCraft. It will explain problems of controlling game entities and bots by computer and introduce some often used solutions. Based on analysis, a new system has been designed and implemented. It uses multi-agent systems to control the game, utilizes machine learning methods and is capable of overcoming oponents and adapting to new challenges.

This diploma thesis explores subjectivity in the domain of so-called digital writing, that is, in texts of largely experimental nature generated by computer algorithms (or with their assistance). In order to do so, the thesis briefly covers the history of digital writing, its mediatic specificities, poetics as well as various theoretical and philosophical conceptualizations. Most importantly, it undertakes an analysis of a post-lyrical subject, a concept devised by Janez Strehovec, that is common to all cases of generative writing under focus. For its comparative analysis, the thesis deals with the recent works from contemporary creators who approach algorithmic textuality from variegated perspectives, incl. Nick Montfort, Allison Parish, Stephanie Strickland, Li Zilles, and Jörg Piringer. Texts generated by programs are conceived of as expressing a new, parasitic and prosthetic, genus of cyber-textual subjectivity that defies the traditional lyric and expands its pool "by other means," as Marjorie Perloff would say. Such a tendency results in conceptually as well as formally complex literary corpus "infected" by - to further exploit the suggested metaphor - parasitic voices and prosthetic selves. Unlike in generic lyric, the post- lyrical subject surpasses the confines of poetry as genre; it is...