Global ETD Search

1	Dual bayesian and morphology-based approach for markerless human motion capture in natural interaction environments Correa Hernandez, Pedro 30 June 2006 (has links) This work presents a novel technique for 2D human motion capture using a single non calibrated camera. The user's five extremities (head, hands and feet) are extracted, labelled and tracked after silhouette segmentation. As they are the minimal number of points that can be used in order to enable whole body gestural interaction, we will henceforth refer to these features as crucial points. The crucial point candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region which lie on the silhouette boundary. In order to disambiguate the selected crucial points into head, left and right foot, left and right hand classes, we propose a Bayesian method that combines a prior human model and the intensities of the tracked crucial points. Due to its low computational complexity, the system can run at real-time paces on standard Personal Computers, with an average error rate range between 2% and 7% in realistic situations, depending on the context and segmentation quality. Human feature extraction Tracking Silhouette Bayesian Mathematical morphology Motion capture
2	Automatic Speech Quality Assessment in Unified Communication : A Case Study / Automatisk utvärdering av samtalskvalitet inom integrerad kommunikation : en fallstudie Larsson Alm, Kevin January 2019 (has links) Speech as a medium for communication has always been important in its ability to convey our ideas, personality and emotions. It is therefore not strange that Quality of Experience (QoE) becomes central to any business relying on voice communication. Using Unified Communication (UC) systems, users can communicate with each other in several ways using many different devices, making QoE an important aspect for such systems. For this thesis, automatic methods for assessing speech quality of the voice calls in Briteback’s UC application is studied, including a comparison of the researched methods. Three methods all using a Gaussian Mixture Model (GMM) as a regressor, paired with extraction of Human Factor Cepstral Coefficients (HFCC), Gammatone Frequency Cepstral Coefficients (GFCC) and Modified Mel Frequency Cepstrum Coefficients (MMFCC) features respectively is studied. The method based on HFCC feature extraction shows better performance in general compared to the two other methods, but all methods show comparatively low performance compared to literature. This most likely stems from implementation errors, showing the difference between theory and practice in the literature, together with the lack of reference implementations. Further work with practical aspects in mind, such as reference implementations or verification tools can make the field more popular and increase its use in the real world. speech voice communication qoe quality of experience unified communication uc speech quality assessment speech quality voice calls gaussian mixture model gmm gaussian mixture regression gmr mel frequency cepstrum coefficients mfcc human feature cepstrum coefficients hfcc gfcc Software Engineering Programvaruteknik

Search results

Dual bayesian and morphology-based approach for markerless human motion capture in natural interaction environments

Automatic Speech Quality Assessment in Unified Communication : A Case Study / Automatisk utvärdering av samtalskvalitet inom integrerad kommunikation : en fallstudie