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Variational Bayesian Subgroup Adaptive Sparse Component Extraction for Diagnostic Imaging System
2018
IEEE transactions on industrial electronics (1982. Print)
A novel unsupervised sparse component extraction algorithm is proposed for detecting micro defects when employing a thermography imaging system. The proposed approach is developed using the Variational Bayesian framework. This enables a fully automated determination of the model parameters and bypasses the need for human intervention in manually selecting the appropriate image contrast frames. An internal sub-sparse grouping mechanism and adaptive fine-tuning strategy have been built to control
doi:10.1109/tie.2018.2801809
fatcat:mz3ljl2qhzbgfkuoavbhk4qvjm