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» Algorithmic issues in modeling motion
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EMMCVPR
2009
Springer
13 years 12 months ago
Reconstructing Optical Flow Fields by Motion Inpainting
An edge-sensitive variational approach for the restoration of optical flow fields is presented. Real world optical flow fields are frequently corrupted by noise, reflection artifac...
Benjamin Berkels, Claudia Kondermann, Christoph S....
CGF
2008
340views more  CGF 2008»
13 years 8 months ago
Articulated Object Reconstruction and Markerless Motion Capture from Depth Video
We present an algorithm for acquiring the 3D surface geometry and motion of a dynamic piecewise-rigid object using a single depth video camera. The algorithm identifies and tracks...
Yuri Pekelny, Craig Gotsman
DAGM
2010
Springer
13 years 8 months ago
Complex Motion Models for Simple Optical Flow Estimation
The selection of an optical flow method is mostly a choice from among accuracy, efficiency and ease of implementation. While variational approaches tend to be more accurate than lo...
Claudia Nieuwenhuis, Daniel Kondermann, Christoph ...
CVIU
2006
222views more  CVIU 2006»
13 years 8 months ago
Conditional models for contextual human motion recognition
We present algorithms for recognizing human motion in monocular video sequences, based on discriminative Conditional Random Field (CRF) and Maximum Entropy Markov Models (MEMM). E...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...
CVPR
2007
IEEE
14 years 10 months ago
Discriminative Learning of Dynamical Systems for Motion Tracking
We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...
Minyoung Kim, Vladimir Pavlovic