Bardia Yousefi

TL;DR: The proposed approach uses a shorter computational alternative to estimate covariance matrix and Singular Value Decomposition to obtain the result of Principal Component Thermography (PCT) and ultimately segments the defects in the specimens applying color based K-medoids clustering approach.

TL;DR: This work presents an application of a new multiscale data analysis method, the Iterative Filtering (IF), which allows to describe the multiscales nature of an electromagnetic signal working in the long-wave infrared (LWIR) region.

TL;DR: The proposed approach focuses on the application of low-rank sparse principal component thermography (Sparse-PCT or SPCT) to assess the advantages and drawbacks of the method for non-destructive testing and demonstrates the considerable performance while the other methods failed.

TL;DR: The proposed approach here focuses on application of some known factor analysis methods such as standard nonnegative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by nonnegative least squares active-set algorithm ( SNMF2) and eigen-decomposition approaches such as principal component analysis (PCA) in thermography to obtain the thermal features.

TL;DR: Empirical results on two aforementioned datasets indicate a promising performance for application of heating and cooling based active thermography with a reasonable computational cost due to unsupervised nature of the algorithm.