Header logo is


2011


no image
Simulation einer fast kritischen binären Flüssigkeit in einem Temperaturgradienten

Single, F.

Universität Stuttgart, Stuttgart, 2011 (mastersthesis)

icm

[BibTex]

2011


[BibTex]


no image
Struktur dichter ionischer Flüssigkeiten

Dannenmann, O.

Universität Stuttgart, Stuttgart, 2011 (mastersthesis)

icm

[BibTex]

[BibTex]


no image
Parallelisierung Stokesscher Dynamik für Graphikprozessoren zur Simulation kolloidaler Suspensionen

Kopp, M.

Universität Stuttgart, Stuttgart, 2011 (mastersthesis)

icm

[BibTex]

[BibTex]


no image
Diffusion in Wandnähe

Müller, J.

Universität Stuttgart, Stuttgart, 2011 (mastersthesis)

icm

[BibTex]

[BibTex]


Benchmark datasets for pose estimation and tracking
Benchmark datasets for pose estimation and tracking

Andriluka, M., Sigal, L., Black, M. J.

In Visual Analysis of Humans: Looking at People, pages: 253-274, (Editors: Moesland and Hilton and Kr"uger and Sigal), Springer-Verlag, London, 2011 (incollection)

ps

publisher's site Project Page [BibTex]

publisher's site Project Page [BibTex]


Steerable random fields for image restoration and inpainting
Steerable random fields for image restoration and inpainting

Roth, S., Black, M. J.

In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

Abstract
This chapter introduces the concept of a Steerable Random Field (SRF). In contrast to traditional Markov random field (MRF) models in low-level vision, the random field potentials of a SRF are defined in terms of filter responses that are steered to the local image structure. This steering uses the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure. Analysis of the statistics of these steered filter responses in natural images leads to the model proposed here. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random fields with anisotropic regularization and provides a statistical motivation for the latter. Steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.

ps

publisher site [BibTex]

publisher site [BibTex]

2005


no image
Interplay between geometry and fluid properties

König, P.-M.

Universität Stuttgart, Stuttgart, 2005 (phdthesis)

icm

link (url) [BibTex]

2005


link (url) [BibTex]


no image
Molecular dynamics of wet granular media

Goll, C.

Universität Stuttgart, Stuttgart, 2005 (mastersthesis)

icm

[BibTex]

[BibTex]


no image
The Boolean Model: from Matheron till today

Stoyan, D., Mecke, K.

In Space, Structure and Randomness: contributions in honor of Georges Matheron in the fields of geostatistics, random sets, and mathematical morphology, 183, pages: 151-182, Lecture Notes in Statistics, Springer, New York, 2005 (incollection)

icm

[BibTex]

[BibTex]


no image
Grenzflächenfluktuationen binärer Flüssigkeiten

Hiester, T.

Universität Stuttgart, Stuttgart, 2005 (phdthesis)

icm

link (url) [BibTex]

link (url) [BibTex]