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ECCV
1994
Springer
13 years 11 months ago
Markov Random Field Models in Computer Vision
A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is dened as the maximum a posteriori (MAP) probability estimate...
Stan Z. Li
CLUSTER
2007
IEEE
13 years 11 months ago
Identifying energy-efficient concurrency levels using machine learning
Abstract-- Multicore microprocessors have been largely motivated by the diminishing returns in performance and the increased power consumption of single-threaded ILP microprocessor...
Matthew Curtis-Maury, Karan Singh, Sally A. McKee,...
ICCV
2011
IEEE
12 years 7 months ago
Revisiting Radiometric Calibration for Color Computer Vision
We present a study of radiometric calibration and the incamera imaging process through an extensive analysis of more than 10,000 images from over 30 cameras. The goal is to invest...
Hai Ting Lin, Seon Joo Kim, Sabine Susstrunk, Mich...
ENGL
2007
160views more  ENGL 2007»
13 years 7 months ago
Diagnosis and Classification of Epilepsy Risk Levels from EEG Signals Using Fuzzy Aggregation Techniques
— This paper is intended to compare the performance of four different types of fuzzy aggregation methods in classification of epilepsy risk levels from EEG Signal parameters. The...
R. Sukanesh, R. Harikumar
ROMAN
2007
IEEE
179views Robotics» more  ROMAN 2007»
14 years 1 months ago
A Bayesian Network Framework for Vision Based Semantic Scene Understanding
— For a robot to understand a scene, we have to infer and extract meaningful information from vision sensor data. Since scene understanding consists in recognizing several visual...
Seung-Bin Im, Keum-Sung Hwang, Sung-Bae Clio