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» Non-linear Bayesian Image Modelling
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CVPR
2000
IEEE
14 years 9 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
CVPR
2001
IEEE
14 years 9 months ago
Flexible flow for 3D nonrigid tracking and shape recovery
We introduce linear methods for model-based tracking of nonrigid 3D objects and for acquiring such models from video. 3D motions and flexions are calculated directly from image in...
Matthew Brand, Rahul Bhotika
3DOR
2008
13 years 10 months ago
Markov Random Fields for Improving 3D Mesh Analysis and Segmentation
Mesh analysis and clustering have became important issues in order to improve the efficiency of common processing operations like compression, watermarking or simplification. In t...
Guillaume Lavoué, Christian Wolf
PAMI
2002
149views more  PAMI 2002»
13 years 7 months ago
Region Tracking via Level Set PDEs without Motion Computation
Tracking regions in an image sequence is a challenging and di cult problem in image processing and computer vision, and at the same time, one that has many important applications:...
Abdol-Reza Mansouri
ECCV
2004
Springer
14 years 9 months ago
Towards Intelligent Mission Profiles of Micro Air Vehicles: Multiscale Viterbi Classification
In this paper, we present a vision system for object recognition in aerial images, which enables broader mission profiles for Micro Air Vehicles (MAVs). The most important factors ...
Sinisa Todorovic, Michael C. Nechyba