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135
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CVPR
2007
IEEE
16 years 5 months ago
Learning Gaussian Conditional Random Fields for Low-Level Vision
Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented u...
Marshall F. Tappen, Ce Liu, Edward H. Adelson, Wil...
149
Voted
ICCV
2007
IEEE
16 years 5 months ago
DynamicBoost: Boosting Time Series Generated by Dynamical Systems
Boosting is a remarkably simple and flexible classification algorithm with widespread applications in computer vision. However, the application of boosting to nonEuclidean, infini...
René Vidal, Paolo Favaro
257
Voted
ICDE
2008
IEEE
195views Database» more  ICDE 2008»
16 years 5 months ago
LOCUST: An Online Analytical Processing Framework for High Dimensional Classification of Data Streams
Abstract-- In recent years, data streams have become ubiquitous because of advances in hardware and software technology. The ability to adapt conventional mining problems to data s...
Charu C. Aggarwal, Philip S. Yu
94
Voted
MICCAI
2007
Springer
16 years 4 months ago
Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy
In this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of "sharpness" and the joint registration-segmentation o...
B. T. Thomas Yeo, Mert R. Sabuncu, Rahul Desikan, ...
125
Voted
EVOW
2010
Springer
15 years 10 months ago
Enhancing Genetic Algorithms by a Trie-Based Complete Solution Archive
Genetic algorithms (GAs) share a common weakness with most other metaheuristics: Candidate solutions are in general revisited multiple times, lowering diversity and wasting preciou...
Günther R. Raidl, Bin Hu