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131
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CVPR
2007
IEEE
16 years 4 months ago
Utilizing Variational Optimization to Learn Markov Random Fields
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Marshall F. Tappen
ICPR
2004
IEEE
16 years 3 months ago
Selective Sampling Based on the Variation in Label Assignments
In this paper, a new selective sampling method for the active learning framework is presented. Initially, a small training set ? and a large unlabeled set ? are given. The goal is...
Piotr Juszczak, Robert P. W. Duin
118
Voted
TEC
2008
98views more  TEC 2008»
15 years 2 months ago
Opposition-Based Differential Evolution
Evolutionary Algorithms (EAs) are well-known optimization approaches to cope with non-linear, complex problems. These population-based algorithms, however, suffer from a general we...
Shahryar Rahnamayan, Hamid R. Tizhoosh, Magdy M. A...
CVPR
2004
IEEE
16 years 4 months ago
A Variational Approach to Scene Reconstruction and Image Segmentation from Motion-Blur Cues
In this paper we are interested in the joint reconstruction of geometry and photometry of scenes with multiple moving objects from a collection of motion-blurred images. We make s...
Paolo Favaro, Stefano Soatto
123
Voted
CVPR
2008
IEEE
16 years 4 months ago
Adaptive and constrained algorithms for inverse compositional Active Appearance Model fitting
Parametric models of shape and texture such as Active Appearance Models (AAMs) are diverse tools for deformable object appearance modeling and have found important applications in...
George Papandreou, Petros Maragos