Masterarbeit MSTR-0003

Bibliograph.
Daten
Maurer, Daniel: Depth-Driven Variational Methods for Stereo Reconstruction.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Masterarbeit Nr. 3 (2014).
73 Seiten, englisch.
CR-Klassif.I.2.10 (Vision and Scene Understanding)
I.4.8 (Image Processing and Computer Vision Scene Analysis)
Kurzfassung

Stereo reconstruction belongs to the fundamental problems in computer vision, with the aim of reconstructing the depth of a static scene. In order to solve this problem the corresponding pixels in both views must be found. A common technique is to minimize an energy (cost) function. Therefore, most methods use a parameterization in form of a displacement information (disparity). In contrast, this thesis uses, extends and examines a depth parameterization. (i) First a basic depth-driven variational method is developed based on a recently presented method of Basha et al. [2]. (ii) After that, several possible extensions are presented, in order to improve the developed method. These extensions include advanced smoothness terms that incorporate image information and enable an anisotropic smoothing behavior. Further advanced data terms are considered, which use modified constraints to allow a more accurate estimation in different situations. (iii) Finally, all extensions are compared with each other and with a disparity-driven counterpart.

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Abteilung(en)Universität Stuttgart, Institut für Visualisierung und Interaktive Systeme, Visualisierung und Interaktive Systeme
BetreuerBruhn, Andrés
Eingabedatum2. Dezember 2014
   Publ. Informatik