Direkt zum Inhalt springen
Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

Menu

Links

Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More


Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Next revision
Previous revision
Last revision Both sides next revision
research:optical_flow_estimation [2012/01/20 11:58]
127.0.0.1 external edit
research:optical_flow_estimation [2015/09/24 21:16]
hazirbas
Line 1: Line 1:
 ====== Optical Flow Estimation ====== ====== Optical Flow Estimation ======
  
-Contact: [[members:steinbrf|Frank Steinbrücker]]+ 
 +Estimating the motion of every pixel in a sequence of images is a problem with many applications in computer vision, such as image segmentation, object classification,visual odometry, and driver assistance. 
 + 
 +In general, optical flow describes a sparse or dense vector field, where a displacement vector is assigned to certain pixel position, that points to where that pixel can be found in another image. 
 +In the context of scene flow estimation, which is performed on images with additional depth values, every pixel is assigned a depth displacement as well. 
 + 
 +Since much of the structural information of a 3D scene gets lost in the imaging process, so does the motion information. The estimation of the "correct" projected motion in an image sequence is therefore highly ill-posed and has to be aided by additional priors such as the regularity of the motion. 
 +Our group mainly focuses on optical flow estimation by means of variational methods, that allow a clear formulation of the assumptions incorporated into the estimation process and generally produce dense vector fields. 
 + 
 +Contact: [[members:hazirbas|Caner Hazırbaş]], [[members:haeusser|Philip Häusser]], [[members:steinbrf|Frank Steinbrücker]]
  
 ====== Related publications ====== ====== Related publications ======

Rechte Seite

Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More