Polygonal Mesh Segmentation

Tato práce se zabývá metodami automatické segmentace polygonálního modelu. K implementaci byla vybrána metoda vycházející z metod segmentace polygonálního modelu podle Gaussovy křivosti a segmentace podle hlavních rysů (feature-sensitive segmentation). Vybraná metoda je dále rozšířena, testována a na závěr jsou její výsledky porovnány s výsledky jiných automatických segmentací a manuálně anotovanými modely.This bachelor's thesis deals with methods of polygonal mesh segmentation. The method which was chosen for implementation is based on existing methods of segmentation driven by Gaussian curvature and feature-sensitive segmentation. The chosen method is further extended, tested and finally its results are compared with results of other automatic segmentations and with manually segmented meshes.

Code Switching Detection in Speech

Tato práce se zabývá problematikou detekce změny jazyka při hovoru. V první části jsou popsány v současnosti používané metody diarizace jazyků. K implementaci byla vybrána metoda založená na akustickém přístupu identifikace jazyka s využitím směsi Gaussovských rozložení, i-vektoru a lineární diskriminační analýzy. Pro experimenty byla vytvořena mandarínsko-anglická databáze se střídáním jazyků. Na této databázi zvolený systém dosahuje úspěšnosti 89,3 % správně klasifikovaných segmentů.This master's thesis deals with code-switching detection in speech. The state-of-the-art methods of language diarization are described in the first part of the thesis. The proposed method for implementation is based on acoustic approach to language identification using combination of GMM, i-vector and LDA. New Mandarin-English code-switching database was created for these experiments. Using this system, accuracy of 89,3 % is achieved on this database.

MixedEmotions: An open-source toolbox for multi-modal emotion analysis

Recently, there is an increasing tendency to embed the functionality of recognizing emotions from the user generated contents, to infer richer profile about the users or contents, that can be used for various automated systems such as call-center operations, recommendations, and assistive technologies. However, to date, adding this functionality was a tedious, costly, and time consuming effort, and one should look for different tools that suits one's needs, and should provide different interfaces to use those tools. The MixedEmotions toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: (i) for text processing: emotion and sentiment recognition, (ii) for audio processing: emotion, age, and gender recognition, (iii) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation, and (iv) for linked data: knowledge graph. Moreover, the MixedEmotions Toolbox is open-source and free. In this article, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standardized test-beds showing its state-of-the-art performance. Furthermore, three real-world use-cases show its effectiveness, namely emotion-driven smart TV, call center monitoring, and brand reputation analysis.peer-reviewe