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CVPR
2000
IEEE
16 years 6 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
ICPR
2008
IEEE
16 years 5 months ago
Accelerating active contour algorithms with the Gradient Diffusion Field
Active contours were proposed by Kass et al. as a way to represent the contours of an image. Although the method is simple, one of its shortcomings is its inability to converge in...
Willie Kiser, Pradeep Sen, Chris Musial
SGP
2007
15 years 6 months ago
Elastic secondary deformations by vector field integration
We present an approach for elastic secondary deformations of shapes described as triangular meshes. The deformations are steered by the simulation of a low number of simple mass-s...
Wolfram von Funck, Holger Theisel, Hans-Peter Seid...
CVPR
2004
IEEE
16 years 6 months ago
Wavelet-Based Hierarchical Surface Approximation from Height Fields
This paper presents a novel hierarchical approach to triangular mesh generation from height fields. A waveletbased multiresolution analysis technique is used to estimate local sha...
Sang-Mook Lee, Daniel L. Schmoldt
ICPR
2006
IEEE
16 years 5 months ago
Adaptative Markov Random Fields for Omnidirectional Vision
Images obtained with catadioptric sensors contain significant deformations which prevent the direct use of classical image treatments. Thus, Markov Random Fields (MRF) whose usefu...
Cédric Demonceaux, Pascal Vasseur