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In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.
Convex programming, Active learning, Measures of information, entropy, Entropy, entropy maximization, Wildfire Detection Using Video, Video Recording, Pattern Recognition, Automated, Disasters, Computer-Assisted, online system, Photography, Projections Onto Convex Sets, Projections onto convex sets, article, methodology, 006, artificial intelligence, Machine vision and scene understanding, automated pattern recognition, classification, Online Learning, Online learning, disaster, Set theory, image subtraction, fire, Algorithms, Automated, online learning, Active Learning, Pattern Recognition, Online Systems, Sensitivity and Specificity, Fires, Wildfire detection, wildfire detection using video, Artificial Intelligence, Image Interpretation, Computer-Assisted, Image analysis in multivariate analysis, image enhancement, reproducibility, Image Interpretation, projections onto convex sets, algorithm, Entropy maximization, Classification and discrimination; cluster analysis (statistical aspects), videorecording, Reproducibility of Results, computer assisted diagnosis, decision fusion, Image Enhancement, photography, Wildfire detection using video, sensitivity and specificity, Decision Fusion, Subtraction Technique, Entropy Maximization, Computer vision, Decision fusion, Image processing (compression, reconstruction, etc.) in information and communication theory
Convex programming, Active learning, Measures of information, entropy, Entropy, entropy maximization, Wildfire Detection Using Video, Video Recording, Pattern Recognition, Automated, Disasters, Computer-Assisted, online system, Photography, Projections Onto Convex Sets, Projections onto convex sets, article, methodology, 006, artificial intelligence, Machine vision and scene understanding, automated pattern recognition, classification, Online Learning, Online learning, disaster, Set theory, image subtraction, fire, Algorithms, Automated, online learning, Active Learning, Pattern Recognition, Online Systems, Sensitivity and Specificity, Fires, Wildfire detection, wildfire detection using video, Artificial Intelligence, Image Interpretation, Computer-Assisted, Image analysis in multivariate analysis, image enhancement, reproducibility, Image Interpretation, projections onto convex sets, algorithm, Entropy maximization, Classification and discrimination; cluster analysis (statistical aspects), videorecording, Reproducibility of Results, computer assisted diagnosis, decision fusion, Image Enhancement, photography, Wildfire detection using video, sensitivity and specificity, Decision Fusion, Subtraction Technique, Entropy Maximization, Computer vision, Decision fusion, Image processing (compression, reconstruction, etc.) in information and communication theory
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