Conjugate Mixture Models for the Modeling of Visual and Auditory Perception - TEL - Thèses en ligne Accéder directement au contenu
Thèse Année : 2010

Conjugate Mixture Models for the Modeling of Visual and Auditory Perception

Résumé

In this thesis, the modelling of audio-visual perception with a head-like device is considered. The related problems, namely audio-visual calibration, audio-visual object detection, localization and tracking are addressed. A spatio-temporal approach to the head-like device calibration is proposed based on probabilistic multimodal trajectory matching. The formalism of conjugate mixture models is introduced along with a family of efficient optimization algorithms to perform multimodal clustering. One instance of this algorithm family, namely the conjugate expectation maximization (ConjEM) algorithm is further improved to gain attractive theoretical properties. The multimodal object detection and object number estimation methods are developed, their theoretical properties are discussed. Finally, the proposed multimodal clustering method is combined with the object detection and object number estimation strategies and known tracking techniques to perform multimodal multiobject tracking. The performance is demonstrated on simulated data and the database of realistic audio-visual scenarios (CAVA database).
Pas de résumé français

Mots clés

Fichier principal
Vignette du fichier
KhalidovThesis.pdf (8.75 Mo) Télécharger le fichier

Dates et versions

tel-00584080 , version 1 (07-04-2011)
tel-00584080 , version 2 (12-12-2012)

Identifiants

  • HAL Id : tel-00584080 , version 1

Citer

Vasil Khalidov. Conjugate Mixture Models for the Modeling of Visual and Auditory Perception. Human-Computer Interaction [cs.HC]. Université Joseph-Fourier - Grenoble I, 2010. English. ⟨NNT : ⟩. ⟨tel-00584080v1⟩

Collections

UJF
296 Consultations
512 Téléchargements

Partager

Gmail Facebook X LinkedIn More