Variational Bayes Sub-Group Adaptive Sparse Component Extraction for Diagnostic Imaging System

Abstract

A novel unsupervised sparse component extraction algorithm for diagnosing micro defects in thermography imaging system is presented. The approach is optimized under Variational Bayesian framework, which is fully automated and does not require manual selection of the parameters in the solution. An internal sub sparse grouping mechanism and adaptive fine-tuning have been built into the proposed algorithm to control the sparsity. The proposed method is used to automatically detect the micro defects on metals. Other contending defect feature extraction and sparse pattern extraction methods are employed for comparison. The algorithm has been shown to improve the detection precision of both artificial and natural cracks.