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Informatik IX
Chair of Computer Vision & Artificial Intelligence

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

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research:optical_flow_estimation [2012/01/20 11:58]
127.0.0.1 external edit
research:optical_flow_estimation [2012/06/08 10:24]
steinbrf
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 ====== Optical Flow Estimation ====== ====== Optical Flow Estimation ======
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 +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.
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 +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.
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 +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:​steinbrf|Frank Steinbrücker]] Contact: [[members:​steinbrf|Frank Steinbrücker]]

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

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

Follow us on:
CVG Group DVL Group